AI Talent · GenAI & Agentic AI Hiring

    Hire GenAI & Agentic AI Engineers in India

    GenAI, LLM, RAG, agentic AI, AI agent, AI application, AI systems and forward deployed engineers, plus GenAI and agentic architects, assessed on production ownership: RAG, context engineering, evaluation, tool calling, MCP, orchestration, memory, security, observability, cost and enterprise integration.

    Discuss a GenAI or Agent Hire See what we test

    In brief

    GenAI & Agentic AI hiring at NeoIntelli covers GenAI, LLM, RAG, agentic AI, AI agent, AI application, AI systems, AI product and forward deployed engineers, and GenAI and agentic architects in India. Candidates are sourced by specialist recruiters, screened and interviewed on NeoHireX, and validated by practicing engineers on RAG, context engineering, evaluation, tool calling, MCP, orchestration, memory and state, security, observability, latency and cost, and enterprise integration. Framework names on a resume are not treated as evidence.

    What we test

    Eleven things a production GenAI or agent engineer must have done.

    These are the topics of the senior technical round. Candidates are asked to show evidence from systems they owned, not to define terms.

    • RAG
    • Context engineering
    • Evaluation
    • Tool calling
    • MCP
    • Agent orchestration
    • Memory and state
    • Security and permissions
    • Observability and tracing
    • Latency and cost
    • Enterprise integration

    Getting the role right

    RAG is not agentic AI. A GenAI engineer is not an ML engineer.

    Most mis-hires in this space start with a role definition that mixes four different jobs. These distinctions are the first thing role calibration settles.

    Distinctions between GenAI and agentic AI roles
    DistinctionWhat it means for the hire
    RAG vs agentic AIRetrieval augments a model's answer with your data. An agent plans, calls tools and acts. RAG is often a component of an agent; it is not the same skill set.
    GenAI Engineer vs ML EngineerA GenAI engineer builds on foundation models: context, retrieval, evaluation, integration. An ML engineer trains and operates models. Some people do both; most do one well.
    AI Agent Developer vs Agentic AI ArchitectA developer builds and ships agents inside a defined design. An architect defines the agent platform, permissions model, evaluation and governance for many agents.
    Prototype experience vs production ownershipA prototype proves an idea. Production ownership means evaluation, permissions, tracing, cost control, incidents and users. Only the second predicts success in your team.

    Role profiles

    What each role owns, and how we tell ownership from exposure.

    GenAI and LLM engineering

    Engineers who build products on large language models. The gap between a demo and a production system is the whole evaluation.

    GenAI Engineer / LLM Engineer

    Owns: LLM-powered features in production: prompt and context design, retrieval, structured outputs, evaluation, cost and latency, and the integration into the product and its data.

    Production signals we look for

    • A feature in front of real users with an evaluation set behind it
    • Retrieval and context decisions explained with measurements
    • Cost per request and latency budgets they managed
    • Failure handling: fallbacks, guardrails, escalation

    Resume signals that mislead

    • Chat-with-your-PDF demos presented as production RAG
    • Prompt tinkering described as engineering
    • Framework lists (LangChain, LlamaIndex) with no system behind them

    How NeoIntelli evaluates

    • Deep dive on one shipped feature: data, retrieval, evaluation, cost
    • Design exercise with quality, latency and cost targets
    • Discussion of an evaluation they built and what it caught

    RAG Engineer

    Owns: Retrieval systems: ingestion and parsing, chunking, embeddings, hybrid search, re-ranking, citation, permission-aware access, refresh pipelines and retrieval evaluation.

    Production signals we look for

    • Retrieval quality measured and improved, not assumed
    • Permission and freshness problems solved in production
    • Clear reasoning on chunking, hybrid search and re-ranking trade-offs

    Resume signals that mislead

    • Vector database familiarity presented as retrieval expertise
    • No experience with permission-aware retrieval or stale indexes

    How NeoIntelli evaluates

    • Retrieval design exercise on messy enterprise documents
    • Evaluation methodology for retrieval precision and faithfulness
    • Debugging scenario for a system that answers confidently and wrongly

    AI Application Engineer / AI Product Engineer

    Owns: Product-grade software with AI components: APIs, UI states for uncertainty, background jobs, data handling, observability and the engineering rigor of any production service.

    Production signals we look for

    • Strong software engineering with AI as one component
    • UX decisions for uncertain outputs and human review
    • Observability and testing for non-deterministic behaviour

    Resume signals that mislead

    • Frontend or backend engineers rebranded without AI ownership
    • AI experience limited to hackathons

    How NeoIntelli evaluates

    • Engineering fundamentals review
    • Design exercise on an AI feature end to end
    • Testing and observability discussion for LLM behaviour

    Agentic AI engineering

    Agents act on systems. The evaluation covers permissions, tracing, evaluation and escalation as much as orchestration.

    Agentic AI Engineer / AI Agent Developer

    Owns: Multi-step, tool-using agents in production: orchestration, tool calling and MCP integration, RAG and context engineering, memory and state, permission handling, evaluation, tracing, human escalation and deployment.

    Production signals we look for

    • An agent in production with a registry entry, permissions and traces
    • Task-success evaluation they designed and ran across versions
    • Incidents they handled: runaway loops, wrong tool calls, cost spikes
    • Escalation paths to humans that actually fire

    Resume signals that mislead

    • Listing LangChain, CrewAI, AutoGen or MCP without a production agent
    • Multi-agent demos with no permissions, evaluation or tracing
    • Prototype experience presented as production ownership

    How NeoIntelli evaluates

    • Walkthrough of one production agent: tools, permissions, evaluation, failures
    • Design exercise for an agent acting on enterprise systems with safety constraints
    • Tool-calling and state-management questions with edge cases

    AI Systems Engineer

    Owns: The systems around models and agents: gateways, routing, caching, queues, rate limits, observability, cost controls and reliability for AI workloads.

    Production signals we look for

    • AI traffic they kept reliable under load
    • Routing and caching decisions with measured savings
    • Tracing and observability stacks they built for LLM or agent calls

    Resume signals that mislead

    • Platform engineers with no exposure to non-deterministic workloads
    • Reliability experience unrelated to model or agent traffic

    How NeoIntelli evaluates

    • Systems design for an LLM gateway with routing and fallbacks
    • Incident scenario on an AI service
    • Cost and latency discussion with numbers

    Forward Deployed AI Engineer

    Owns: Delivering AI systems inside a customer's environment: discovery, integration with their data and workflows, evaluation against their criteria and adoption after launch.

    Production signals we look for

    • Deployments in customer environments with adoption outcomes
    • Integration with unfamiliar data and systems under time pressure
    • Communication with non-technical stakeholders

    Resume signals that mislead

    • Solutions engineers with demo-only experience
    • Delivery experience without hands-on engineering

    How NeoIntelli evaluates

    • Scenario on integrating an AI feature into a customer's stack
    • Engineering depth check
    • Stakeholder communication assessment

    GenAI and agentic architecture

    GenAI Architect / Agentic AI Architect

    Owns: Platform and system design for LLM applications and agents: model strategy, retrieval and agent architecture, evaluation and governance integration, security, cost and the guidance of the engineers building on it.

    Production signals we look for

    • Architectures in production for multiple use cases, with reuse
    • Governance, security and cost designed in from the start
    • Decisions they reversed after evaluation evidence

    Resume signals that mislead

    • Vendor reference architectures presented as experience
    • Architect titles with no production accountability

    How NeoIntelli evaluates

    • Architecture review of a multi-agent enterprise scenario
    • Deep questions on evaluation, governance and cost design
    • Technical leadership assessment

    We interview. You hire.

    The validation flow behind every GenAI and agent shortlist.

    1. STEP 01

      Role calibration

      Understand what this person actually has to build, own and operate, and at what seniority.

    2. STEP 02

      Specialist sourcing

      Search the AI, data and platform talent pools relevant to the role, not a generic database.

    3. STEP 03

      NeoHireX screening

      Role-calibrated screening and ranking on NeoIntelli's own Hiring OS.

    4. STEP 04

      AI first-round interview

      Structured candidate evaluation before a human hour is spent.

    5. STEP 05

      Senior technical round

      Practicing AI and data engineers assess technical depth against the role.

    6. STEP 06

      Expert validation

      Validate ownership, architecture decisions and production experience behind the resume.

    7. STEP 07

      Qualified shortlist

      You see evaluation context and evidence, not another stack of CVs.

    8. STEP 08

      You decide

      Your team runs the final interviews and makes the hiring decision. NeoHireX never does.

    The same production disciplines the technical round tests are what an AI GCC runs day to day. See Generative & Agentic AI for GCCs and MLOps, LLMOps & AgentOps.

    Buyer questions

    Questions about hiring GenAI and agentic AI engineers in India.

    How do you evaluate a GenAI engineer?

    On a shipped feature: the data behind it, retrieval and context decisions, the evaluation set, cost and latency, and failure handling. Practicing GenAI engineers run the round and the evidence is recorded in NeoHireX with the shortlist.

    GenAI Engineer vs Agentic AI Engineer?

    A GenAI engineer builds LLM features that answer or generate. An agentic AI engineer builds systems that plan and act through tools, which adds permissions, state, tracing, evaluation of task success and human escalation to the skill set.

    Is listing LangChain, CrewAI, AutoGen or MCP enough?

    No. Framework names show exposure. We look for a production agent with a registry entry, tool permissions, traces, an evaluation set and an incident the candidate handled. That is what the technical round asks about.

    Can you hire agentic AI engineers for a regulated industry?

    Yes. The evaluation adds emphasis on permissions, auditability, data handling and human oversight, and the shortlist notes how each candidate has worked under those constraints.

    How does GenAI hiring connect to building an AI GCC?

    When the first GenAI hire becomes a team, NeoIntelli can run the operating environment around it. Generative & Agentic AI for GCCs covers the engineering the team will do; the AI Micro GCC model covers people operations, workspace, IT and infrastructure.

    Discuss a GenAI or agent hire.

    Tell us what the system does, which tools it touches and how it is evaluated today. We will calibrate the role against that architecture and show you the technical round before sourcing starts.

    Discuss a GenAI or Agent Hire Explore Generative & Agentic AI for GCCs

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