Machine Learning Engineer

8 years

0 Lacs

Posted:4 hours ago| Platform: Linkedin logo

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On-site

Job Type

Full Time

Job Description

Location:

Experience:

Role Overview:

AI/ML Developer

Key Responsibilities:

  • Design and implement machine learning models for predictive analytics, NLP, computer vision, and recommendation systems.
  • Develop and fine-tune

    Large Language Models (LLMs)

    and

    Generative AI

    applications.
  • Build scalable ML pipelines using

    MLOps

    best practices (CI/CD for ML).
  • Optimize models for performance, accuracy, and cost efficiency.
  • Collaborate with data engineers and product teams to integrate ML solutions into production systems.
  • Stay updated with the latest AI/ML trends and research to drive innovation.

Required Skills & Qualifications:

  • Strong programming skills in

    Python

    (and optionally

    R

    ,

    Java

    , or

    C++

    ).
  • Expertise in ML frameworks:

    TensorFlow

    ,

    PyTorch

    ,

    Scikit-learn

    ,

    Hugging Face Transformers

    .
  • Experience with

    Generative AI

    ,

    LLMs

    , and

    Prompt Engineering

    .
  • Knowledge of

    Deep Learning

    ,

    NLP

    ,

    Computer Vision

    , and

    Reinforcement Learning

    .
  • Familiarity with

    MLOps tools

    :

    MLflow

    ,

    Kubeflow

    ,

    Airflow

    ,

    Docker

    ,

    Kubernetes

    .
  • Cloud experience:

    AWS SageMaker

    ,

    Azure ML

    , or

    Google Vertex AI

    .
  • Strong understanding of

    data preprocessing

    ,

    feature engineering

    , and

    model evaluation

    .
  • Proficiency in

    API development

    and

    microservices architecture

    for ML deployment.

Nice-to-Have Skills:

  • Experience with

    Vector Databases

    (e.g., Pinecone, Weaviate) for AI search.
  • Knowledge of

    Graph Neural Networks (GNNs)

    and

    Federated Learning

    .
  • Familiarity with

    Responsible AI

    ,

    Bias Mitigation

    , and

    Explainable AI (XAI)

    .
  • Exposure to

    Edge AI

    and

    IoT-based ML solutions

    .

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