Senior Machine Learning Engineer

Experience: 6 – 10 years
Location: Hyderabad - Work from Office
Department: Enterprise Data & AI
Reports To: Director – AI/ML Solutions

Core Technical Expertise

• Programming & Scripting: Strong Python and Bash scripting expertise.
• ML Algorithms & Frameworks: Proficiency in ML algorithms (XGBoost, LightGBM, CatBoost) and frameworks (TensorFlow, PyTorch, Keras).
• ML Deployment & Operations (MLOps):

Proven experience in designing and deploying ML models at scale.
Experience with feature stores, model serving, and ML orchestration tools.Expertise in data pipeline orchestration (Airflow, Prefect, or Kubeflow).Experience with feature engineering, model versioning, and A/B testing.Strong understanding of machine learning lifecycle management and observability.

• Infrastructure & DevOps:

Familiarity with containerization (Docker, Kubernetes) and cloud platforms (AWS, Azure, or GCP).
Hands-on with Git, CI/CD, REST APIs, and API gateways.

• Data & Databases: Knowledge of SQL/NoSQL databases, data lakes, and vector databases.

Professional & Leadership Skills

• Proven ability to lead and mentor ML engineering teams.
• Excellent troubleshooting, performance tuning, and incident resolution skills.
• Excellent communication, analytical thinking, and collaboration skills.

Nice-to-Have Skills

• Experience with LLM fine-tuning, RAG (Retrieval-Augmented Generation), or Generative AI pipelines.
• Familiarity with MCP (Model Context Protocol) and agent orchestration frameworks (LangChain, LangGraph, or Haystack).
• Exposure to data governance frameworks and ML observability tools (e.g., Arize, EvidentlyAI).
• Understanding of business use cases in FinTech, Healthcare, or Supply Chain analytics.

Job Type: Full-time

Pay: ₹272,589.86 - ₹1,523,983.80 per year

Application Question(s):

• How many years of professional experience do you have specifically in Machine Learning Engineering (MLE) or MLOps?
• Have you successfully designed and deployed ML models at scale in a production environment? (Yes/No)
• Which programming languages are you proficient in for ML development and infrastructure scripting?
• Which tool(s) have you used to orchestrate ML/Data pipelines in a professional setting?
• Which platforms and tools are you experienced with for building and running production ML systems?

Work Location: In person