Machine Learning Engineer

5 years

0 Lacs

Posted:2 days ago| Platform: Linkedin logo

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On-site

Job Type

Contractual

Job Description

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Role Overview

intelligent systems

This role requires strong technical depth, independent ownership, and the ability to collaborate with cross-functional teams across engineering, IT, and business domains.

Key Responsibilities

  • Lead the design, development, and deployment of AI/ML models across vision, language, and prediction use cases
  • Develop and implement solutions using

    LLMs

    (e.g., LLaMA, Azure OpenAI) and

    Computer Vision

    frameworks (OpenCV, PyTorch, YOLO, etc.)
  • Architect

    hybrid deployment models

    using

    Azure cloud

    and

    on-premises infrastructure

    , ensuring high availability and scalability
  • Collaborate with internal stakeholders using

    design thinking

    to convert business needs into AI solutions
  • Develop end-to-end ML pipelines (ETL, training, evaluation, inference, monitoring)
  • Optimize performance of models for real-time inference and resource efficiency (GPU/CPU)
  • Ensure best practices in

    CI/CD, containerization (Docker/Kubernetes), and model lifecycle management

  • Support internal teams with documentation, training, and handover of AI tools and platforms

Required Qualifications & Experience

  • B.Tech from a premier engineering institute

    ;

    M.Tech preferred

  • 5+ years of relevant experience in AI/ML engineering and production deployments
  • Proficiency in

    Python

    ,

    PyTorch

    , and deep learning tools
  • Strong experience with

    LLMs

    (e.g., LLaMA, GPT) and

    vision analytics

    for object detection, classification, and OCR
  • Proven ability to deploy models on

    Azure and on-prem environments

    with GPU acceleration
  • Familiarity with

    predictive modelling techniques

    in manufacturing/engineering contexts
  • Excellent communication and stakeholder engagement skills


Desirable Skills (Good to Have)

  • Experience with

    digital twin models

    ,

    edge computing

    , or

    smart factory systems

  • Knowledge of

    Hugging Face

    ,

    vLLM

    , or other open-source LLM frameworks
  • Exposure to

    MLOps tools

    , API development (FastAPI), and automated model retraining
  • Awareness of

    data security

    ,

    model interpretability

    , and

    ethical AI practices


Soft Skills & Role Readiness

  • Candidate should be

    self-driven

    and able to work

    independently with minimal guidance

  • Strong verbal and written

    communication skills

    to engage with business and technical teams
  • Ability to

    translate requirements

    into technical solutions using

    design thinking principles

  • Comfortable with working in a

    manufacturing/industrial environment

    if required onsite


Supplier Responsibilities

  • Conduct a

    technical screening and validation

    before sharing profiles.
  • Provide

    reference checks

    and past project verification (especially for AI/ML deployment experience)
  • Take ownership of

    resource onboarding

    ,

    handover coordination

    , and

    periodic performance feedback collection and assistance

  • Provide a

    backfill plan

    in case of early attrition or performance misalignment

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