To apply: Share your resumes on khushboo.a@mitrhr.com, muskan.d@mitrhr.com and rashmita.r@mitrhr.com
📍 Location: Bangalore
💼 Experience: 8–10 Years
💰 Compensation: As per industry standards
About the Role
We are looking for a highly skilled Machine Learning Engineer to fine-tune, optimize, and deploy ML and AI models at scale. The ideal candidate will have deep experience with LLMs, MLOps, and model lifecycle management, working across tools like MLflow, Databricks, and LangChain to deliver high-performing AI solutions for enterprise use.
Key Responsibilities
- Fine-tune, optimize, and retrain ML/AI models, including large language models (LLMs) and enterprise-grade ML systems.
- Build and maintain evaluation pipelines to test model accuracy, robustness, fairness, and efficiency.
- Implement MLOps best practices and automate end-to-end workflows using MLflow and CI/CD pipelines.
- Conduct feature engineering and data transformations for improved model performance.
- Design model monitoring and feedback systems to track post-deployment performance and drift.
- Run A/B tests and benchmarking experiments to validate model improvements.
- Collaborate with data scientists and AI developers to productionize models efficiently.
- Utilize Databricks Mosaic AI, Azure ML, or AWS SageMaker for scalable ML workloads.
- Integrate explainability and fairness checks (SHAP, LIME, InterpretML) into the ML lifecycle.
Required Skills & Expertise
✅ 8–10 years of experience in Machine Learning Engineering or related fields.
✅ Strong proficiency in Python and frameworks like PyTorch, TensorFlow, and scikit-learn.
✅ Hands-on experience with MLflow, MLOps pipelines, and cloud-based ML platforms (Azure ML / SageMaker).
✅ Familiarity with evaluation frameworks (DeepEval, RAGAS, or custom evaluation setups).
✅ Good SQL skills and ability to work with both structured and unstructured data.
✅ Exposure to Spark / Databricks for distributed data processing.
✅ Understanding of deep learning architectures, model explainability, and bias mitigation techniques.
✅ Knowledge of LangChain/LangGraph for AI evaluation and testing.
Good to Have
- Experience in LLM fine-tuning and vector database integration (Pinecone, Chroma, Weaviate).
- Familiarity with RAG (Retrieval-Augmented Generation) systems.
- Exposure to containerized ML deployments using Docker/Kubernetes.
- Understanding of enterprise-grade data governance and security practices.
Why Join Us
Join a forward-thinking organization building next-generation AI and ML systems that transform business intelligence, automation, and decision-making. Work with a passionate, innovation-driven team on cutting-edge AI solutions designed for scalability and impact.