Machine Learning Engineer

About the Role

We are looking for an experienced Machine Learning Engineer proficient in developing and deploying AI/ML solutions using AWS. The position involves creating agentic AI systems, fine-tuning large language models (LLMs), and managing operational ML workflows to build scalable, real-world intelligent systems. You will collaborate with cross-functional data and MLOps teams and have the chance to work with advanced AWS AI/ML services.

Working Locations: Bangalore, Hyderabad, Chennai, Mumbai, Pune, Ahmedabad
Work Mode: Hybrid, with a mandatory two days per week onsite presence.

Key Responsibilities

• Design and implement machine learning models focused on agentic AI, including LLM orchestration, retrieval-augmented generation (RAG), and embedding techniques.
• Develop and optimize data preprocessing workflows to handle structured, unstructured, and streaming data sources effectively.
• Perform fine-tuning of LLMs, build embeddings, and develop RAG pipelines.
• Deploy ML models into production through APIs and integrated applications.
• Work closely with data engineers to ensure high-quality, scalable training datasets.
• Collaborate with MLOps teams to automate the entire ML lifecycle including training, testing, deployment, monitoring, and governance.
• Assess and refine models for improved accuracy, scalability, cost-effectiveness, and operational performance.
• Conduct experiments, including A/B testing, to validate model reliability.
• Maintain comprehensive documentation covering experiments, processing pipelines, and best methods to ensure reproducibility.

Required Skills & Qualifications

• Between 4 to 6 years of experience in machine learning engineering.
• Strong command of Python programming, including libraries such as NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow.
• In-depth knowledge of all stages in the ML lifecycle including data preprocessing, training, and deployment.
• Hands-on familiarity with AWS AI/ML services such as SageMaker, Bedrock, Lambda, Step Functions, ECS/EKS, S3, DynamoDB, and Kinesis.
• Experience with natural language processing, large language models, embeddings, and vector search technologies.
• Proficiency in ML workflow orchestration tools like Airflow, Kubeflow, or MLflow.
• Understanding of API development, microservice deployment, and cloud-native system integration.
• Experience working with SQL and NoSQL databases, including vector database solutions like Weaviate, Pinecone, and FAISS.
• Knowledge of data version control, experiment tracking, and maintaining a model registry.

Desirable Skills

• Experience using multi-agent AI frameworks such as LangChain, Haystack, or LlamaIndex.
• Exposure to generative AI applications like chatbots, autonomous agents, and intelligent document processing.
• Familiarity with continuous integration and continuous deployment (CI/CD) practices for ML and DevOps.
• Awareness of model explainability, bias identification, and responsible AI principles.

Additional Information

This listing is compiled from an external source; role details may vary and candidates should verify the most current information directly with the company.