AI Talent · MLOps & AI Platform Hiring

    Hire MLOps & AI Platform Engineers in India

    MLOps, ML platform, AI platform, AI infrastructure, AI systems, LLMOps, AgentOps, GPU infrastructure and AI DevSecOps engineers, assessed on the production systems they have operated and the incidents they have handled.

    Discuss an MLOps or Platform Hire See role distinctions

    In brief

    MLOps & AI Platform hiring at NeoIntelli covers MLOps, ML platform, AI platform, AI infrastructure, AI systems, LLMOps, AgentOps, GPU infrastructure and AI DevSecOps engineers in India. These are the roles that move AI from experimentation to production. Specialist recruiters source, NeoHireX screens and runs first-round AI interviews, and practicing platform engineers validate candidates on systems they operate, incidents they handled and the cost and reliability numbers behind them.

    Getting the role right

    MLOps, LLMOps, AgentOps, platform and infrastructure are five jobs, not one.

    Distinctions between MLOps and AI platform roles
    DistinctionWhat it means for the hire
    MLOps Engineer vs AI Platform EngineerMLOps runs the lifecycle of models. A platform engineer builds the shared infrastructure many teams and lifecycles use. Small teams start with one person doing both; scale separates them.
    MLOps vs LLMOps vs AgentOpsMLOps operates trained models. LLMOps adds prompts, retrieval, generated-output evaluation, routing and cost per call. AgentOps adds tools, permissions, traces, task success and escalation.
    AI Infrastructure Engineer vs AI Platform EngineerInfrastructure provisions and operates the compute, storage and network. Platform builds the services and developer experience on top of it.

    Role profiles

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

    MLOps and ML platform

    MLOps Engineer

    Owns: The lifecycle of production models: registry and versioning, CI/CD for training and deployment, evaluation gates, monitoring, drift detection, retraining and rollback.

    Production signals we look for

    • Pipelines that deploy models with evaluation gates, and a rollback they performed
    • Drift or quality monitoring that caught a real problem
    • Reproducible training from code and data

    Resume signals that mislead

    • DevOps engineers with no model lifecycle exposure
    • MLflow or Kubeflow familiarity without a production model behind it

    How NeoIntelli evaluates

    • Walkthrough of one model lifecycle they run end to end
    • Design exercise for CI/CD with evaluation gates and rollback
    • Incident scenario: silent quality decay in production

    ML Platform Engineer / AI Platform Engineer

    Owns: Shared platform capabilities for AI teams: training and serving infrastructure, feature and vector stores, experiment tracking, model gateways, developer environments and the internal APIs teams build on.

    Production signals we look for

    • Platform capabilities adopted by more than one team
    • Serving infrastructure with stated latency and cost
    • Developer experience improvements with adoption evidence

    Resume signals that mislead

    • Single-team tooling described as a platform
    • Cloud certifications without a platform they operated

    How NeoIntelli evaluates

    • Platform design exercise for multi-team AI workloads
    • Operations scenario: capacity, cost, outage
    • Developer-experience and API design discussion

    LLMOps and AgentOps

    Production operations for LLM applications and agents. The roles are new; the evidence we ask for is not.

    LLMOps Engineer

    Owns: Prompt and retrieval configuration lifecycle, evaluation pipelines for generated output, model routing and fallbacks, cost per call, tracing and quality monitoring for LLM applications.

    Production signals we look for

    • Prompt regression caught by an evaluation they built
    • Routing or caching decisions with measured cost impact
    • Tracing across model and retrieval calls in production

    Resume signals that mislead

    • Prompt engineering presented as LLMOps
    • Observability experience unrelated to non-deterministic workloads

    How NeoIntelli evaluates

    • Design exercise for continuous evaluation of an LLM feature
    • Cost and routing discussion with numbers
    • Incident scenario on quality regression after a model update

    AgentOps Engineer

    Owns: Registry, versioning, tool permissions, tracing, task-success evaluation, human escalation, cost per task and incident handling for autonomous and semi-autonomous agents.

    Production signals we look for

    • An agent they operate with a registry entry, permissions and traces
    • Task-success evaluation across agent versions
    • Incidents handled: runaway loops, wrong tool calls, cost spikes

    Resume signals that mislead

    • Agent framework demos with no operations behind them
    • MLOps experience assumed to transfer without agent exposure

    How NeoIntelli evaluates

    • Walkthrough of an agent in production: permissions, evaluation, incidents
    • Design exercise for agent governance controls
    • Escalation and kill-switch scenario

    AI Systems Engineer

    Owns: Reliability of AI services: gateways, queues, rate limits, caching, autoscaling, observability and cost control for model and agent traffic.

    Production signals we look for

    • AI traffic kept reliable under load with numbers
    • Caching and routing with measured savings
    • Observability stacks built for LLM or agent calls

    Resume signals that mislead

    • SRE experience with no AI workload exposure
    • Reliability claims without incidents to discuss

    How NeoIntelli evaluates

    • Systems design for an LLM gateway with fallbacks
    • Incident scenario on an AI service
    • Capacity and cost discussion

    AI infrastructure and DevSecOps

    AI Infrastructure Engineer / GPU Infrastructure Engineer

    Owns: Compute for AI: GPU capacity planning, cloud, reserved and dedicated environments, training and inference clusters, storage and networking that keep accelerators fed, utilization and AI FinOps.

    Production signals we look for

    • GPU environments they provisioned and operated with utilization data
    • Storage and network decisions that fixed a training bottleneck
    • Cost allocation and reservation management

    Resume signals that mislead

    • Cloud infrastructure experience with no accelerator workloads
    • Kubernetes experience assumed to cover GPU scheduling

    How NeoIntelli evaluates

    • Capacity planning exercise from a workload description
    • Troubleshooting scenario: idle GPUs, slow data loading
    • FinOps discussion with a utilization model

    AI DevOps / DevSecOps Engineer

    Owns: Delivery pipelines, identity and secrets, isolation for sensitive AI workloads, supply-chain and dependency controls, and security integration for AI systems.

    Production signals we look for

    • Security controls applied to model, data and agent access
    • Pipelines that enforce policy without blocking delivery
    • Incidents or audits they supported

    Resume signals that mislead

    • Generic DevOps with no AI-specific controls
    • Security checklists without implementation

    How NeoIntelli evaluates

    • Design exercise for secure AI delivery with isolation and secrets
    • Policy-as-code and identity discussion
    • Audit-evidence scenario

    We interview. You hire.

    The validation flow behind every MLOps and platform 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 operating layer these engineers build is described under MLOps, LLMOps & AgentOps, and the compute they run it on under AI Infrastructure & GPU.

    Buyer questions

    Questions about MLOps and AI platform recruitment in India.

    MLOps Engineer vs AI Platform Engineer: what is the difference?

    An MLOps engineer runs the lifecycle of production models: CI/CD, evaluation gates, monitoring, retraining, rollback. An AI platform engineer builds the shared infrastructure and services that multiple AI teams use. A first team often hires one person for both and separates the roles as workloads grow.

    What is an AgentOps engineer?

    Someone who operates AI agents in production: registry and versioning, tool permissions, tracing, task-success evaluation, human escalation, cost per task and incident handling. The role is new; the evidence we ask for is a production agent they have operated and an incident they handled.

    Why are MLOps and AI platform roles hard to hire?

    The skills sit between software engineering, infrastructure and machine learning, and most candidates have depth in one and exposure to the others. Role calibration decides which depth matters for your stack, and the technical round checks it with operations scenarios rather than tool quizzes.

    Do we need a GPU infrastructure engineer?

    Only when utilization, isolation or latency justify reserved or dedicated GPU environments. Teams on cloud on-demand capacity usually need an AI platform engineer who understands compute cost first. The decision is made from the workload, not the hardware.

    How does this connect to an AI GCC?

    These roles run the production layer of an AI GCC. See MLOps, LLMOps & AgentOps for the operating layer they build and AI Infrastructure & GPU for the compute they run it on.

    Discuss an MLOps or platform hire.

    Tell us what runs in production today, what is stuck in pilot and how releases happen. We will calibrate the role to that stack and show you the operations scenarios the technical round uses.

    Discuss an MLOps or Platform Hire Explore MLOps, LLMOps & AgentOps

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