Machine Learning Engineer

About the Role

We are seeking a Machine Learning Engineer with hands-on experience in designing and deploying AI/ML solutions on AWS. You will build agentic AI systems, fine-tune LLMs, and operationalize ML workflows for real-world applications. This role offers the opportunity to work with cutting-edge AWS AI/ML services, collaborate across data and MLOps teams, and deliver production-ready intelligent systems at scale.

Location: Bangalore, Hyderabad, Chennai, Mumbai, Pune, Ahmedabad

Work Mode: Hybrid (2 days work from office is mandate)

Key Responsibilities

• Design, develop, and deploy ML models for agentic AI use cases (LLM orchestration, RAG, embeddings).
• Build and optimize data preprocessing pipelines for structured, unstructured, and streaming data.
• Implement LLM fine-tuning, embeddings, and retrieval-augmented generation (RAG) pipelines.
• Integrate ML models into production APIs and applications.
• Collaborate with data engineers to ensure clean, reliable, and scalable training datasets.
• Partner with MLOps teams to automate training, testing, deployment, monitoring, and model governance.
• Evaluate and optimize models for accuracy, scalability, cost-efficiency, and performance.
• Conduct experimentation, A/B testing, and validation to ensure reliability.
• Maintain documentation of experiments, pipelines, and best practices for reproducibility.

Required Skills & Qualifications

• 4-6 years of ML engineering experience (flexible based on seniority).
• Strong programming skills in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
• Solid understanding of the ML lifecycle (data preprocessing training deployment).
• Hands-on experience with AWS AI/ML services: SageMaker, Bedrock, Lambda, Step Functions, ECS/EKS, S3, DynamoDB, Kinesis.
• Familiarity with large language models (LLMs), NLP, embeddings, and vector search.
• Experience with ML pipeline orchestration tools (Airflow, Kubeflow, MLflow, or similar).
• Knowledge of APIs, microservice deployment, and cloud-native integration.
• Proficiency with SQL/NoSQL databases and vector databases (Weaviate, Pinecone, FAISS).
• Understanding of data versioning, experiment tracking, and model registry.

Nice-to-Have

• Experience with multi-agent AI frameworks (LangChain, Haystack, LlamaIndex).
• Exposure to generative AI use cases (chatbots, autonomous agents, document intelligence).
• Familiarity with CI/CD for ML and DevOps practices.
• Knowledge of model explainability, bias detection, and responsible AI practices.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.