Sr Machine Learning Engineer - AI Systems

ABOUT AMGEN

Amgen harnesses the best of biology and technology to fight the worlds toughest diseases, and make peoples 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 whats known today.

ABOUT THE ROLE

Role Description:

We are seeking a highly skilled and experienced Senior Machine Learning Engineer with strong experience in designing, configuring, and building agentic AI systems, cloud-native software applications, and ML-enabled solutions. In this role, you will develop scalable software systems that leverage large language models, intelligent agents, workflow orchestration, APIs, cloud services, and MLOps practices.

The ideal candidate combines strong software engineering fundamentals with practical experience in AI/ML systems, including agent frameworks, retrieval-augmented generation, model integration, evaluation, deployment, monitoring, and automation. You will work closely with product teams, data scientists, ML engineers, platform teams, and business stakeholders to deliver secure, reliable, and maintainable AI-enabled software solutions.

Roles Responsibilities:

• Design, configure, and build agentic AI systems that can reason, plan, use tools, execute workflows, and interact with enterprise systems.
• Develop scalable software applications and services using modern cloud-native architectures.
• Integrate large language models, APIs, databases, vector stores, and orchestration frameworks into production-ready applications.
• Build and maintain AI-enabled workflows using agent frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or similar technologies.
• Implement retrieval-augmented generation patterns, including document ingestion, chunking, embedding generation, vector search, reranking, and response generation.
• Partner with data science and ML teams to operationalize machine learning models and AI capabilities into software products.
• Apply MLOps practices for model deployment, monitoring, versioning, evaluation, governance, and continuous improvement.
• Develop reusable components, APIs, services, and integration patterns to accelerate AI solution delivery.
• Define and implement robust cloud architectures, preferably on AWS, using serverless, containerized, or microservices-based approaches.
• Implement observability, logging, monitoring, error handling, and performance optimization for AI and ML-enabled applications.
• Evaluate and improve agent performance, including prompt quality, tool selection, response accuracy, latency, cost, and reliability.

Functional Skills:

Must-Have Skills:

• Strong hands-on software engineering experience with Python, JavaScript/TypeScript, SQL/NoSQL, APIs, and cloud-native application development.
• Experience building or integrating applications with generative AI, LLMs, agentic workflows, RAG patterns, or AI orchestration frameworks.
• Working knowledge of ML lifecycle concepts, including model deployment, evaluation, monitoring, versioning, and automation.
• Experience with CI/CD, testing, containerization, observability, and production-grade software delivery.
• Strong problem-solving, communication, collaboration, and technical ownership skills.

Good-to-Have Skills:

• Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar agent frameworks.
• Experience with vector databases, embeddings, semantic search, reranking, prompt engineering, fine-tuning, or LLM evaluation.
• Experience with MLOps tools such as MLflow, SageMaker, Databricks, Kubeflow, Airflow, or similar platforms.
• Familiarity with serverless, microservices, event-driven architecture, Kubernetes, or managed cloud AI/ML services.
• Knowledge of responsible AI, model governance, human-in-the-loop workflows, security, privacy, and regulated environments.

Education and Professional Certifications

• Bachelors or Master’’s degree in computer science and engineering preferred, other Engineering field is considered; 7 to 12 years of software development experience
• Cloud Computing certificate preferred

Soft Skills:

• Excellent analytical and troubleshooting skills.
• Strong verbal and written communication skills
• Ability to work e…