Senior Machine Learning Engineer

Senior Machine Learning Engineer (AI/ML Focus)

Work Mode: Hybrid (Kolkata)

Salary Package: 45  LPA

Key Responsibilities

• Lead the end-to-end development of scalable AI/ML solutions—from research and prototyping to deployment and monitoring—solving complex business problems.
• Design, build, and maintain robust ML pipelines and APIs, ensuring seamless integration of models into production systems and applications.
• Spearhead projects across key AI domains, including NLP, Computer Vision, and Generative AI (e.g., LLMs, RAG applications), to deliver measurable impact.
• Establish and champion MLOps best practices, leveraging tools like Docker, Kubernetes, MLflow, and Azure to ensure model reproducibility, scalability, and automated CI/CD workflows.
• Collaborate cross-functionally with Data Engineering, Product, and DevOps teams to align AI initiatives with business goals and architectural standards.
• Mentor junior data scientists/engineers and contribute to the team's technical strategy and knowledge base.

Required Qualifications

• 5+ years of professional experience in building, deploying, and maintaining production-grade AI/ML systems.
• Expert proficiency in Python and core ML libraries (PyTorch/TensorFlow, scikit-learn, pandas). [Note: Removed Django/Flask from "strong proficiency" - see below].
• Proven hands-on experience in at least two of the following: Deep Learning, NLP, Computer Vision, or Generative AI/LLM applications.
• Solid experience with modern MLOps tooling: Docker, Git, CI/CD, and at least one major cloud platform (Azure preferred; AWS/GCP acceptable).
• Strong foundation in software engineering for production: version control, testing, APIs, and familiarity with databases (SQL).
• Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.
• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related quantitative field, or equivalent practical experience.

Preferred Skills & Assets

• Direct experience with Generative AI tech stacks (e.g., LangChain, RAG architectures, LLM fine-tuning and deployment) is a significant advantage.
• Experience with data pipeline/orchestration tools (Apache Spark, Airflow, dbt).
• Knowledge of container orchestration (Kubernetes) and model lifecycle management (MLflow).
• A portfolio of projects, contributions to open-source ML projects, or relevant publications.