AI Talent · AI & ML Recruitment

    AI & Machine Learning Recruitment in India

    Hire AI engineers, machine learning engineers, deep learning, NLP and computer vision specialists, research engineers, data scientists and AI solution architects, each assessed on what they have owned in production rather than on the frameworks on their resume.

    Discuss an AI/ML Hire See how we validate

    In brief

    AI & ML recruitment at NeoIntelli covers AI engineers, machine learning engineers, applied and deep learning engineers, NLP and computer vision engineers, AI research engineers, data scientists and AI solution architects in India. Specialist recruiters source, NeoHireX screens and runs first-round AI interviews, and practicing ML engineers assess model development, data pipelines, training and evaluation, productionization, inference and monitoring before you see a shortlist. You make the final decision.

    Role profiles

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

    Job titles in AI are unreliable. These profiles describe the work, the evidence that someone has done it, and the resume patterns that look like evidence but are not.

    Machine learning engineering

    Engineers who take a model from data to a monitored production system. The evaluation covers the whole lifecycle, not the modelling step alone.

    Machine Learning Engineer

    Owns: Model development, the feature and data pipeline it depends on, training and evaluation, productionization, inference and monitoring, with software engineering discipline throughout.

    Production signals we look for

    • A model they trained that is still serving traffic, and what happened when it drifted
    • Feature pipelines they built or debugged, not only consumed
    • Evaluation beyond a single offline metric: slices, baselines, online checks
    • Inference constraints they solved: latency, batch vs real time, cost

    Resume signals that mislead

    • Kaggle or course projects listed as production experience
    • Framework lists with no system attached
    • Team-level outcomes claimed as personal ownership

    How NeoIntelli evaluates

    • Walk through one system end to end: data, model, deployment, monitoring
    • Trade-off questions on feature freshness, retraining cadence, serving
    • Code and design review on a realistic problem
    • Reference-style questions on what broke and who fixed it

    AI Engineer / Applied ML Engineer

    Owns: Applying existing models and APIs to product problems: integration, evaluation, data handling and the engineering around the model rather than novel model research.

    Production signals we look for

    • Shipped features where the model is one component of a larger system
    • Evaluation sets they built for the product task
    • Clear reasoning about when to use an API, a fine-tune or classical ML

    Resume signals that mislead

    • Demo apps that call an API with no evaluation or error handling
    • Titles inflated from software engineer to AI engineer without AI ownership

    How NeoIntelli evaluates

    • Product-scenario design exercise with cost and quality constraints
    • Review of an evaluation approach they designed
    • Engineering fundamentals: APIs, data handling, testing

    Deep Learning Engineer

    Owns: Designing, training and optimizing neural networks for vision, language, speech or multimodal tasks, including data, compute and inference efficiency.

    Production signals we look for

    • Training runs they owned on real hardware, with the failures
    • Architecture or training changes that measurably improved a result
    • Quantization, distillation or serving optimizations in production

    Resume signals that mislead

    • Paper reimplementations presented as original work
    • GPU experience limited to notebooks on shared clusters

    How NeoIntelli evaluates

    • Deep dive on one training project: data, architecture, compute, outcome
    • Debugging scenario for a failing or unstable run
    • Inference efficiency discussion tied to a serving target

    Specialist AI engineering

    NLP Engineer

    Owns: Language systems: classification, extraction, search, summarization and conversational components, increasingly built on LLMs but still needing evaluation and data rigor.

    Production signals we look for

    • Production text systems with measured precision and recall
    • Domain data handling: annotation, cleaning, evaluation set design
    • Judgment on when an LLM replaces or complements a classical pipeline

    Resume signals that mislead

    • Chatbot demos with no evaluation
    • Library familiarity without a deployed system

    How NeoIntelli evaluates

    • Design exercise on a real language task with quality targets
    • Evaluation dataset design discussion
    • Review of an error analysis they ran

    Computer Vision Engineer

    Owns: Image and video systems from data collection and labelling through model training, deployment on cloud or edge, and monitoring in the field.

    Production signals we look for

    • Deployed vision models with field performance numbers they can explain
    • Data pipeline and labelling strategy ownership
    • Edge or latency constraints solved in production

    Resume signals that mislead

    • Pretrained-model demos on public datasets only
    • No experience with domain shift between lab and field

    How NeoIntelli evaluates

    • End-to-end walkthrough of a deployed vision system
    • Domain-shift and data-quality scenario
    • Optimization discussion for the target hardware

    AI Research Engineer

    Owns: Bridging research and product: reproducing and adapting methods, running rigorous experiments and turning results into engineering the product team can use.

    Production signals we look for

    • Experiments designed with controls and ablations
    • Research results that reached a product
    • Clean, reproducible experimental code

    Resume signals that mislead

    • Publication count without product impact where product impact is the role
    • Research framed as engineering or vice versa

    How NeoIntelli evaluates

    • Discussion of an experiment they designed and what it changed
    • Code review focused on reproducibility
    • Assessment of how they communicate results to engineers

    Data science and architecture

    Data Scientist

    Owns: Turning business questions into analysis, experiments and models with decisions attached: statistics, experimentation, modelling and clear communication of uncertainty.

    Production signals we look for

    • Analyses that changed a decision, with the decision named
    • Experiment design and interpretation with caveats
    • Models handed to engineering with a plan for production

    Resume signals that mislead

    • Dashboards described as data science
    • Modelling with no statistical reasoning behind the choices

    How NeoIntelli evaluates

    • Case exercise from question to recommendation
    • Statistics and experimentation fundamentals
    • Communication of uncertainty to a non-technical stakeholder

    AI Solutions Architect

    Owns: System boundaries, integration patterns, model and vendor selection, scalability, security and cost for enterprise AI solutions, and the technical guidance of the team building them.

    Production signals we look for

    • Architectures in production with the trade-offs they made explicit
    • Security, residency and cost handled as design inputs
    • Experience guiding engineers, not only drawing diagrams

    Resume signals that mislead

    • Reference architectures copied from vendors
    • Architecture experience without production accountability

    How NeoIntelli evaluates

    • Architecture review of a real scenario with constraints
    • Deep questions on decisions they made and would now change
    • Assessment of technical leadership behaviour

    We interview. You hire.

    The validation flow behind every AI/ML 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.

    Buyer questions

    Questions about AI and ML recruitment in India.

    AI Engineer vs ML Engineer: what is the difference?

    An ML engineer trains, deploys and monitors models. An AI engineer applies models, often through APIs or foundation models, inside products, with the emphasis on integration, evaluation and engineering. The titles overlap in the market, so NeoIntelli calibrates the role to what the person will own, not the title.

    Data Scientist vs ML Engineer?

    A data scientist answers business questions with analysis, experiments and models and communicates decisions. An ML engineer builds and operates models as production software. Many teams need one of each before they need two of either.

    How do you assess a machine learning engineer?

    Against the lifecycle: model development, feature and data pipeline understanding, training and evaluation, productionization, inference, monitoring and software engineering. Practicing engineers run the technical round and record the evidence in NeoHireX with the shortlist.

    Can you hire an AI/ML engineer into our own entity, or only into a GCC?

    Either. Permanent hires go directly into your organization or GCC. Where you do not yet have an India entity, the hire can start under a managed or EOR-first model and move later.

    How is this different from a generalist technology recruiter?

    Specialist sourcing, role calibration by AI practitioners and a technical validation round before the shortlist. A generalist recruiter forwards resumes that match keywords. This service forwards candidates whose production ownership has been checked.

    Discuss an AI or ML hire.

    Tell us the system the person will own and the seniority you need. We will calibrate the role, show you the evaluation plan and start sourcing.

    Discuss an AI/ML Hire Explore Generative & Agentic AI for GCCs

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