Job Description

Machine Learning Engineer


Experience: 2 to 4 years

Work Location: Chennai, Madurai & Coimbatore | Hybrid


Job Summary:

We are looking for a Machine Learning Engineer with 2–4 years of experience in building and deploying ML-driven solutions, including ML and GenAI-based systems. The role involves working across the full ML lifecycle, from data preparation and model development to deployment, monitoring, and continuous improvement in production environments.


Key Responsibilities:

  • Build, train, and optimize machine learning and GenAI models for production use cases.
  • Work with large-scale structured and unstructured datasets (text, images, embeddings, tabular data).
  • Implement feature engineering, data preprocessing, and model evaluation pipelines.
  • Develop and deploy ML/GenAI models. Integrate ML solutions with backend systems and applications.
  • Work with embedding models, vector databases, or retrieval-based ML systems where applicable. Monitor model performance, latency, and quality in production.
  • Collaborate closely with data engineers, backend engineers, and product teams.
  • Document model behavior, assumptions, and deployment workflows.


Required Skills & Qualifications:

  • 2+ years of hands-on experience as a Machine Learning Engineer.
  • Solid understanding of machine learning concepts and algorithms.
  • Experience with frameworks such as PyTorch, TensorFlow, scikit-learn, Langchain, LangGraph etc.
  • Familiarity with NLP, embeddings, or deep learning–based models.
  • Hands-on experience with GenAI systems (LLMs, prompt engineering, RAG pipelines, fine tuning)
  • Experience with data processing using Pandas, NumPy, and SQL.
  • Understanding of model deployment, inference, and performance optimization.
  • Experience using Git and working in Linux-based environments.
  • Experience with cloud platforms such as AWS / GCP / Azure.
  • Experience with Docker and containerized deployments.
  • Strong communication skills with the ability to explain ML/GenAI concepts clearly to both technical and non-technical stakeholders.


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