ABOUT AMGEN

Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, making people’s lives easier, fuller, and longer. We discover, develop, manufacture, and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting edge of innovation, using technology and human genetic data to push beyond what’s known today.

ABOUT THE ROLE

Let’s do this. Let’s change the world.
We are seeking a Senior Machine Learning Engineer to join our Manufacturing Applications Product team at Amgen India (AIN). In this vital role, you will lead the design, development, and deployment of advanced machine learning and generative AI solutions for manufacturing and operations use cases. You will collaborate with platform architects, software engineers, and business users to deliver scalable, production-grade ML models and platforms that drive innovation and business impact.

Key Responsibilities

• Lead end-to-end development of machine learning models, including data exploration, feature engineering, model selection, training, validation, and deployment.

• Architect and implement scalable ML pipelines for batch and real-time inference using cloud-native tools (AWS).

• Develop and optimize deep learning models (NLP, computer vision, tabular data) using frameworks such as TensorFlow, PyTorch, and scikit-learn.

• Design, fine-tune and deploy Large Language Models (LLMs) such as GPT-5, for manufacturing applications (RAG copilots, chatbots, summarization, classification, agents).

• Build retrieval-augmented generation (RAG) pipelines with vector databases (e.g., Pinecone, pgvector/Postgres) and, where appropriate, knowledge graph integrations.

• Establish LLMOps best practices: prompt libraries and versioning, offline/online evaluation harnesses, A/B testing, safety/guardrails, cost/latency optimization, and model monitoring.

• Collaborate with cross-functional teams to translate business requirements into technical solutions and deliver measurable outcomes.

• Integrate ML/LLM services with enterprise platforms, APIs, and data lakes for seamless production deployment.

• Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.

• Ensure compliance with data privacy, security, and regulatory requirements (including Responsible AI practices) throughout the ML lifecycle.

• Document solutions, share knowledge, and contribute to Amgen’s AI/ML community of practice.

Must-Have Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field.

• 8+ years of hands-on experience developing and deploying machine learning models in production environments.

• Advanced proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn, XGBoost).

• Experience with cloud platforms (AWS) and containerization (Docker, Kubernetes).

• Proven experience building LLM-powered applications in production, including prompt engineering, fine-tuning, retrieval integration (RAG), and API/SDK-based deployment.

• Experience with MLOps tooling (e.g., MLflow, SageMaker, Vertex AI, Databricks) and model monitoring/observability.

• Strong understanding of data engineering, feature engineering, and model evaluation techniques; ability to define offline/online evaluation metrics for both ML and LLM use cases.

• Excellent problem-solving, communication, and collaboration skills.

Good-to-Have Skills

• Experience with graph-based retrieval augmented generation (GraphRAG), knowledge graphs, or graph databases (e.g., Neo4j, Amazon Neptune, TigerGraph) for enhancing LLM applications.

• Experience applying machine learning or AI solutions in manufacturing, industrial, or operational environments.

• Experience with vector databases (Databricks, Pinecone,pgvector) and orchestration frameworks (LangChain, LlamaIndex).

• Experience with LLM serving and optimization and cost/latency tuning in production.

• Familiarity with tool/function calling, multi-agent orchestration, and workflow frameworks.

• Exposure to regulated domains (biotech, pharma, healthcare), GxP/CSV, and Responsible AI/safety methodologies.

• Contributions to open-source ML/LLM projects, papers, or presentations in ML/AI forums.

Soft Skills

• Analytical and troubleshooting mindset.

• Ability to work effectively in global, virtual teams.

• Initiative, self-motivation, and adaptability.

• Team-oriented, with a focus on achieving shared goals.

• Ability to manage multiple priorities and deliver in a fast-paced environment.