Agentic & Applied AI
Agent builders, AI engineers, agent architects and AI product engineers creating reliable production applications.
Build high-impact AI teams across Generative AI, Agentic AI, Machine Learning, AI Infrastructure and Research with Grizmo Labs—India’s specialist AI recruitment agency and founding-team search partner, from Seed to Series C.
Grizmo Labs combines technical role calibration, targeted market mapping and evidence-led screening across the AI stack—from research and models to production systems, products and governance.
Agent builders, AI engineers, agent architects and AI product engineers creating reliable production applications.
Fine-tuning, post-training, alignment, evaluation and research talent working beyond API integration.
GPU, CUDA, Triton, vLLM, Ray, distributed training and model-serving specialists.
Biometrics, robotics, video intelligence, vision-language, speech and edge-AI engineers.
Red teaming, guardrails, evaluation, privacy, model risk, compliance and responsible AI leaders.
Each mandate is calibrated around named systems, methods, evidence and outcomes—not an inflated list of AI keywords.
Builds tool-using, multi-step systems that plan, act, observe and recover in production.
Turns models into reliable product workflows with strong engineering, evaluation and customer context.
Adapts and aligns named models using real datasets, techniques and measurable model outcomes.
Advances training, post-training, multimodal or reasoning capabilities through rigorous experimentation.
Builds production GenAI systems across retrieval, prompting, orchestration, monitoring and UX.
Creates the platform that makes training, serving, experimentation and governance repeatable.
Owns model lifecycle, pipelines, observability, deployment quality and production reliability.
Designs benchmarks, human and automated evals, failure analysis and red-team programmes.
Protects AI systems against prompt injection, data leakage, unsafe actions and adversarial abuse.
Optimises model serving for throughput, latency, cost and distributed production workloads.
Builds systems combining text, images, documents, speech, audio and video understanding.
Develops perception for biometrics, robotics, detection, autonomous systems and edge deployment.
Creates simulation, augmentation and privacy-aware data strategies for training and evaluation.
Builds knowledge, embedding and data pipelines that make AI systems useful and trustworthy.
Connects enterprise use cases with secure cloud architecture, integration and deployment choices.
The team needed to prove an AI wedge is different from the organisation needed to scale models, products and governance globally.
Build a small team that can move from customer problem to working product.
Strengthen model quality, data, reliability and customer adoption.
Improve inference, evaluation, security and team execution.
Add executive ownership, global delivery and responsible governance.
Our AI screening is designed to distinguish model builders, production engineers, researchers and platform specialists from surface-level tool users.
Define the actual AI problem, evidence bar, stage and non-negotiables.
Identify relevant labs, product companies, teams and research communities.
Engage passive talent with a technically credible opportunity narrative.
Assess named work, outcomes, ownership, motivation and practical constraints.
Support interviews, references, compensation, offer and joining assurance.
AI hiring has moved beyond one generic “machine learning engineer” title. Winning teams need precise combinations of research, engineering, infrastructure, product and responsible deployment.
The AI talent market changes faster than conventional job architectures. A title such as AI Engineer can describe an API-focused application developer, an LLM fine-tuning specialist, an ML systems engineer or a research scientist. Those profiles may use overlapping language while delivering very different value. The cost of confusing them is high: slow product progress, weak evaluation, unreliable systems and senior hires who cannot solve the real problem.
Grizmo Labs begins with the capability the company needs to build. We clarify whether the mandate is product experimentation, foundation-model adaptation, autonomous-agent design, multimodal perception, inference optimisation, platform reliability, evaluation, safety or leadership. We then define the proof that should exist in a credible candidate’s work: named models, techniques, datasets, system constraints, metrics, publications or production outcomes.
This approach improves the search in two ways. It narrows the target market to people who have genuinely solved a comparable problem, and it creates a more credible story for passive candidates. AI specialists are more likely to engage when a recruiter can explain the technical mandate, team, data advantage, infrastructure, product use case and why the role matters.
Agentic AI hiring is one of the most commercially important search areas in 2026. Companies are moving from single-turn assistants to systems that plan, use tools, call APIs, coordinate specialised agents and operate across real workflows. The strongest candidates understand that a demo is not a production system. They can reason about state, memory, tool selection, permissions, failure recovery, observability, evaluation, latency and cost.
For Agentic AI Engineer searches, we look for direct experience with frameworks and patterns such as LangGraph, AutoGen, CrewAI, model context protocols, structured tool calling and multi-agent orchestration. Framework names are only the starting point. Candidates should explain why an architecture was chosen, how actions were constrained, what failed, how quality was evaluated and what business outcome improved.
Applied AI Engineers combine AI knowledge with strong product engineering. They may build retrieval systems, copilots, workflow automation, intelligent search or decision-support products. We assess Python and software-engineering quality, model and API choices, retrieval design, data pipelines, evaluation, monitoring and customer impact. For early-stage companies, we also screen for breadth, speed and comfort working closely with founders and users.
Companies building defensible model capability need more than prompt engineering. LLM Model Engineers work with fine-tuning, post-training, alignment, distillation, quantisation and evaluation. Our screening looks for a specific named model—such as Llama, Mistral, Qwen, BERT, T5, Phi or Mixtral—combined with a real technique such as LoRA, QLoRA, PEFT, SFT, DPO or RLHF. Credible evidence also includes the dataset, objective, experimental design and model-level metric.
We distinguish between “fine-tuning” mentioned in a skill list and a candidate who can explain the training pipeline, compute constraints, hyperparameters, validation approach, failure analysis and improvement achieved. Experience with pretraining, training from scratch, synthetic data, distillation or quantisation-aware training can be especially valuable for advanced mandates.
AI Research Scientist searches may prioritise transformer research, multimodal models, reasoning, pretraining, post-training or efficient learning. Publication history at venues such as ACL, EMNLP, NAACL, NeurIPS, ICML or ICLR can be a strong signal, but research must still match the company’s need. Some clients need original research; others need scientists who can translate research into product experiments and production collaboration.
As usage grows, AI performance becomes a systems problem. AI Platform Engineers build the shared foundation for training, experimentation, model registry, evaluation, deployment and governance. The work can span Kubernetes, Ray, Kubeflow, MLflow, feature or embedding systems, GPU scheduling and internal developer platforms. We look for engineers who have designed reliable capabilities used by other teams, not only administered individual tools.
Inference and Performance Engineers optimise serving across CUDA, Triton, TensorRT, ONNX, vLLM and distributed systems. Their impact is measured through latency, throughput, utilisation, memory efficiency, cost and reliability. Candidates may work on batching, caching, quantisation, kernel optimisation, speculative decoding, parallelism or hardware-aware deployment. The appropriate depth depends on whether the company serves external models, adapted open-source models or proprietary model infrastructure.
LLMOps and MLOps roles connect modelling with production. Strong candidates design repeatable pipelines, CI/CD, monitoring, lineage, rollback, drift detection and incident response. For GenAI systems, observability may also cover prompts, retrieval quality, model versions, token usage, safety failures and evaluation scores. We calibrate whether the role is primarily platform engineering, data and pipeline engineering or model operations.
Evaluation is becoming a core engineering function rather than a final testing step. AI Evaluation Engineers design task-specific benchmarks, golden datasets, automated evaluators, human review, regression suites and failure taxonomies. They may use Ragas, DeepEval, Promptfoo, LangSmith, Braintrust, Arize Phoenix or custom frameworks, but tooling is less important than the quality of the evaluation design.
We assess whether candidates can connect evaluation to product risk. A support copilot, medical coding assistant and autonomous enterprise agent require different measures and escalation policies. Evaluation talent must identify what “good” means, where automated scores are unreliable and how to turn failures into product or model improvements.
AI Security Engineers address prompt injection, jailbreaks, insecure tool use, sensitive-data exposure, poisoned retrieval, model theft and adversarial inputs. They bring together application security, cloud security, ML understanding and red teaming. Governance and Responsible AI leaders add policy, documentation, model inventory, approval workflows, regulatory awareness, bias and risk management. These roles are especially valuable for regulated industries and enterprise products that must earn buyer trust.
Multimodal AI expands the interface beyond text. Engineers work across vision-language models, document intelligence, OCR, speech, audio and video understanding. Searches may require model training, multimodal retrieval, dataset design, evaluation or product integration. We look for evidence that candidates understand the unique data, compute and evaluation issues of the relevant modality.
Computer Vision and Physical AI Engineers build perception for biometrics, robotics, autonomous systems, quality inspection, video intelligence and edge devices. Depending on the mandate, relevant evidence may include detection, segmentation, tracking, face recognition, liveness detection, SLAM, 3D perception or sensor fusion. Production capability can require PyTorch, OpenCV, deployment through TensorRT or ONNX, quantisation, real-time constraints and testing across difficult operating conditions.
Synthetic Data Engineers support cases where real data is scarce, sensitive, expensive or poorly balanced. Their work can include simulation, augmentation, generative data, privacy preservation and scenario coverage. Strong candidates can explain how synthetic data was validated, where distribution gaps emerged and how the strategy affected downstream model performance.
An AI Product Manager must connect model behaviour with customer value. The role requires more than writing requirements for an engineering team. Strong candidates understand the limits of models, design experiments, choose product and model metrics, work with evaluation and create experiences that handle uncertainty gracefully. Agentic AI Product Managers also need to reason about permissions, human oversight, workflow boundaries and the consequences of autonomous actions.
Head of AI, VP AI, Director of Applied AI and Chief AI Officer searches require careful scope definition. The company may need a hands-on technical leader, a research leader, a product and platform executive, or an enterprise transformation owner. Titles alone are unreliable. We define the decisions the leader must own: AI strategy, build-versus-buy choices, team architecture, research direction, data, infrastructure, product adoption, governance or executive customer engagement.
AI Transformation Leads and Solutions Consultants help enterprises move from pilots to deployed value. AI Developer Relations Engineers build trust with technical communities, partners and users. AI Governance, Compliance and Model Risk leaders create the operating controls required for adoption. Grizmo Labs evaluates these candidates against the market, stakeholder environment and maturity of the organisation they will enter.
Demand is strongest where talent scarcity intersects with business-critical ownership. These mandates often justify retained or priority search because the relevant candidates are passive, technically specialised and comparing several compelling opportunities.
Forward-Deployed AI Engineers are particularly valuable for enterprise AI companies. They combine product engineering, AI implementation, customer discovery and rapid deployment. The best candidates can operate inside complex customer environments while feeding repeatable learning back into the core platform.
India offers deep AI and ML talent across Bengaluru, Hyderabad, Pune, NCR, Chennai and emerging distributed hubs. The market includes AI-native startups, global product companies, research labs, cloud and semiconductor teams, enterprise SaaS businesses and specialised services organisations. However, availability varies sharply by skill, seniority, company background, notice period and work model.
Globalink™ helps international companies build high-calibre AI capability from India. We support talent mapping, compensation calibration, role sequencing, market positioning and search. For a new India team, early leadership and senior individual contributors matter disproportionately because they establish technical standards, hiring quality and collaboration norms.
Global teams should also define how the India organisation will participate in product decisions. The strongest candidates are attracted by real ownership, sophisticated problems and access to customers and global leadership—not a narrow execution centre. We help clients communicate that mandate clearly and assess candidates for distributed collaboration, documentation, time-zone overlap and executive communication.
Our screening starts with evidence. For LLM training roles, we seek named models, techniques, datasets and model metrics. For applied AI, we examine architecture, evaluation, production constraints and product outcome. For platform and inference roles, we look for scale, performance, reliability and cost evidence. For researchers, we assess research contribution, experimental rigour and relevance. For leadership, we examine strategy, organisation design, hiring, technical judgement and measurable impact.
We also identify red flags. A resume dominated by LangChain, prompts, RAG and agent frameworks without a clear production or evaluation story may not meet a model-building mandate. “Fine-tuning” listed without a project, model, technique or result is not sufficient evidence. Conversely, a research candidate may be excellent but unsuitable for an early product team that needs broad engineering and customer iteration.
Before submission, we validate motivation, compensation, notice period, location, competing offers and interview availability. The hiring manager receives a concise fit rationale, evidence, gaps and risks. This makes each profile easier to evaluate and reduces time lost on attractive but irrelevant resumes.
Success search works well for clearly defined individual-contributor and manager positions where the talent pool is accessible. Retained search is recommended for leadership, confidential mandates, rare research or systems skills and searches requiring deep market mapping. Project or RPO models support companies building several roles, an AI pod or a new India capability.
Every engagement includes a dedicated SPOC, search calibration, targeted sourcing and a regular review cadence. Our standard operating model targets first relevant profiles within 24 hours after calibration and agreement completion, although research-heavy and confidential searches may require a deeper mapping phase. The objective is not profile volume. It is a focused shortlist with evidence that matches the mandate.
Founders need to know which AI capability is truly strategic and which can be bought, partnered or added later. An early company rarely needs every specialist role at once. It may need one applied AI builder who can move across product, data and evaluation before it needs a platform team. Alternatively, a model-native company may need a research or training specialist at the centre from day one. We help founders sequence hiring around the next proof point, funding milestone and runway rather than copying the structure of a mature AI lab.
For CTOs and engineering leaders, the critical question is often ownership boundary. The team may already have strong software engineers but lack model evaluation, inference performance, data quality or research depth. A precise scorecard defines where the new hire should lead, where they should collaborate and what technical decisions they will own. This reduces role overlap and makes interviews more consistent across engineering, product and leadership stakeholders.
For HR and talent leaders, AI hiring creates a new language and benchmarking challenge. Titles change quickly, compensation can vary sharply and hiring managers may use the same phrase for different capabilities. Grizmo Labs provides market feedback, target-company mapping, evidence-led screening and a shared tracker so the internal team can maintain control without becoming dependent on profile volume. We also surface recurring rejection reasons and candidate objections, helping clients improve role design, interview speed and employer positioning.
A strong interview process tests the work the candidate will actually perform. Generic coding rounds or broad ML trivia may eliminate relevant specialists while allowing polished generalists to progress. The assessment should follow the scorecard created during calibration and combine technical evidence, problem-solving, system judgement, collaboration and ownership.
For an Agentic AI Engineer, a useful discussion may examine how the candidate would design tools, state, permissions, memory, observability and recovery for a real workflow. The interviewer should explore failure modes, evaluation and why an agent is preferable to a deterministic system. For an LLM Model Engineer, the process may include experimental design, data preparation, technique choice, compute trade-offs, validation and interpretation of model metrics. For an inference specialist, it may focus on profiling, bottlenecks, batching, quantisation, hardware constraints and reliability.
Portfolio and project deep dives are particularly important. Candidates should be able to separate their own contribution from the team’s work, explain alternatives considered and describe what changed because of their decisions. Where confidentiality limits details, strong candidates can still explain the problem structure, method and lessons without disclosing sensitive information.
Leadership interviews should test organisational and business judgement in addition to technical credibility. A Head of AI or VP AI may need to decide which capabilities to build, establish an evaluation culture, manage research uncertainty, partner with product, recruit senior talent and communicate with customers, investors or regulators. Case discussions grounded in the company’s current stage reveal more than abstract leadership questions.
Process speed also matters. High-quality AI candidates frequently run several conversations at once. Clear ownership, coordinated panels, rapid feedback and transparent next steps improve both acceptance and employer reputation. Grizmo Labs supports interview scheduling, candidate preparation, expectation alignment and offer closing so momentum is not lost between stages.
AI compensation cannot be benchmarked accurately by title alone. Scarcity, depth, research pedigree, production ownership, location, company stage and competing offers all affect expectations. A Staff Applied AI Engineer, Research Scientist and AI Platform Engineer may each sit in a different market even when their experience duration is similar. Equity can be meaningful for startup candidates, but only when the company explains the opportunity, ownership and value creation clearly.
Motivation is equally important. Some candidates want publication freedom and deep research. Others prefer direct product impact, large-scale infrastructure, customer exposure or the chance to build a team. The search narrative should connect the mandate with the candidate’s desired trajectory. Misalignment cannot be solved by compensation alone and often appears later as offer rejection or early attrition.
Before an offer, we validate compensation expectations, notice period, counteroffer risk, competing processes, location and decision criteria. We help the client shape a complete proposition covering role scope, manager, technical challenge, resources, growth, title, cash and equity. Continued engagement through the notice period supports joining confidence, especially in markets where long notice periods and counteroffers are common.
The best AI candidates evaluate the quality of the problem as carefully as the compensation. They want to understand the data advantage, product access, compute environment, model strategy, technical colleagues, leadership commitment and freedom to make meaningful decisions. A vague promise to “build cutting-edge AI” is less persuasive than a specific explanation of the customer problem, current system, constraints and next technical milestone.
We help clients translate internal context into an accurate candidate narrative. For an AI-native startup, that may include proprietary workflows, expert feedback loops and the opportunity to shape the model and product together. For a global SaaS company, it may include real distribution, enterprise data and the chance to introduce intelligence into a mature platform. For a research-led company, it may include publication goals, compute resources and collaboration with recognised scientists.
Credibility matters more than hype. Candidates should know which parts of the system exist, which are still experimental and what they will be expected to create. Clear communication builds trust, improves interview quality and makes accepted offers more resilient.
There is no universal first-five template. The correct sequence depends on whether the company’s advantage comes from proprietary models, a differentiated workflow, unique data, infrastructure efficiency or distribution into a valuable customer base. Grizmo Labs helps founders identify the smallest team capable of proving the next commercial and technical milestone.
For an application-led AI startup, the first hire may be a founding Applied AI Engineer with strong product engineering, followed by a backend or platform engineer, an AI Product Manager, a data-focused engineer and a customer-facing Forward-Deployed AI Engineer. This sequence prioritises speed to customer value, evaluation and repeatable implementation. A second specialist can be added when the product reveals a genuine model, retrieval, inference or safety bottleneck.
For a model-led company, the nucleus may include an AI Research Scientist, LLM Model Engineer, AI Data Engineer, AI Platform Engineer and Inference Engineer. These hires create the loop between data, experiments, training, deployment and performance. The team still needs product judgement; technical novelty without a clearly measured user or business outcome can consume runway without creating defensibility.
For an enterprise SaaS company adding AI to an existing platform, the early group may combine a Director of Applied AI, Staff Applied AI Engineer, AI Product Manager, Evaluation Engineer and platform or security specialist. These people must integrate with established engineering, product, compliance and customer-success teams. Their success depends on adoption, reliability and trust as much as model quality.
For computer vision, robotics or physical AI companies, the first team is shaped by the sensing environment and deployment target. A perception or vision scientist may work with a data or simulation engineer, edge or inference engineer, robotics software engineer and platform owner. Evaluation must reflect operating conditions such as lighting, device limits, latency, safety, fairness and long-tail scenarios.
The hiring sequence should be revisited after each milestone. Once the initial system works, the next constraint might be evaluation, data quality, serving cost, reliability, security, customer implementation or team leadership. Hiring the role that removes the current constraint produces more leverage than filling a predetermined organisation chart.
Scarce AI searches require a wider map than a list of famous technology companies. Relevant candidates may sit inside research labs, AI-native startups, cloud platforms, semiconductor and GPU organisations, robotics companies, developer-tool businesses, global capability centres, university-linked labs or specialised teams within large enterprises. The strongest adjacent talent may use a different title while solving the same underlying problem.
Our market mapping identifies target organisations, teams, projects, publication communities, open-source contributors and credible adjacencies. We segment prospects by technical depth, company stage, product environment, location, compensation and likely motivation. For confidential leadership searches, this creates a controlled universe and an evidence-based view of candidate availability before outreach scales.
Mapping also helps correct unrealistic assumptions. A client may discover that the desired combination of research pedigree, production ownership, leadership scale and compensation is extremely narrow. The solution might be to separate one role into two, adjust the level, expand geography, reconsider must-have experience or sequence a senior individual contributor before an executive. These decisions are most valuable when made early rather than after weeks of weak pipeline.
General staffing processes are often designed for repeatable titles, broad databases and high submission volume. Specialist AI search requires more interpretation. The recruiter must understand why LoRA experience differs from prompt orchestration, why an inference engineer differs from an MLOps engineer and why an excellent researcher may not fit a product-led founding team.
Grizmo Labs combines founder-level role calibration with targeted headhunting and structured evidence capture. We do not claim to replace the client’s technical interviewers. We make their time more productive by presenting candidates whose work, motivation and operating context have already been examined against the mandate.
Our submissions are designed to answer the first questions a hiring manager would otherwise spend time discovering: What did this person build? Which model or system did they own? What methods did they use? What changed because of their work? At what scale? In what type of team? Why are they interested now? What are the practical constraints and risks?
This creates a better experience for both clients and candidates. Clients review fewer, more relevant profiles. Candidates enter informed conversations where the recruiter can explain the technical challenge and company stage. The result is a search process built around clarity, evidence and mutual fit.
Access specialised AI engineering, research, infrastructure, product and leadership talent through a search partner that understands both the technology and the India market.
An AI recruitment agency identifies and evaluates specialised talent across applied AI, machine learning, LLMs, research, infrastructure, evaluation, security, product and leadership. It distinguishes candidates by the work they have actually delivered rather than generic AI keywords.
We recruit Agentic AI Engineers, Applied AI Engineers, LLM Model Engineers, Research Scientists, AI Platform and Inference Engineers, MLOps/LLMOps, evaluation and safety specialists, AI security, multimodal and computer vision talent, AI product leaders and AI executives.
We look for a named model, a specific method such as LoRA, QLoRA, PEFT, SFT, DPO or RLHF, the dataset or training objective and a model-level metric or rigorous evaluation. A skill-list mention alone is not treated as evidence.
Yes. Grizmo Labs specialises in founding and core-team hiring. We look for high-ownership engineers who can combine product engineering, AI judgement, customer iteration and 0→1 execution.
Yes. Our executive-search practice supports confidential Head of AI, VP AI, Chief AI Officer, Director of Applied AI and AI product leadership mandates.
After role calibration and agreement completion, our standard operating model targets the first relevant profiles within 24 hours. Highly specialised research, executive or confidential searches may require deeper mapping.
Yes. Globalink™ helps international companies build engineering, research, product and leadership capability from India, including talent mapping, compensation calibration and multi-role search.
We recruit across Bengaluru, Hyderabad, Pune, NCR, Chennai and other Indian technology hubs, along with remote and international mandates.
Tell us the model, product or capability you are creating. We’ll architect the talent search.