Sr Machine Learning Engineer

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 helpedestablishthe biotechnology industry more than 40 years ago andremainson thecutting edgeof innovation, using technology and human genetic data to push beyondwhat’sknown today.

ABOUT THE ROLE

Role Description:

Let’sdo this.Let’schange the world. We are looking for a highly motivated expertMachine LearningEngineertodesign and develop scalable, secure, and reliable data pipelines and ingestion solutions that power knowledge layers and assistant experiences via generative AI solutions for our Manufacturing Applications Product Team.TheSenior Machine Learning Engineerwilldesign, build, and scale AI-powered solutions that transform manufacturing operations. This role will focus on developing production-grade GenAI and machine learning systems, including retrieval-augmented generation (RAG), agentic workflows, knowledge layers, and intelligent assistants. The ideal candidate combines deepexpertisein data engineering, distributed computing,MLOps, and modern AI platforms to deliver secure, scalable, and reliable solutions that enable advanced analytics, automation, and decision support across manufacturing operations.

Roles &Responsibilities

• Design, deploy,monitor, andoptimizeproduction-grade ML and Generative AI applications for AI-enabled manufacturing solutions. 

• Define technical architecture, engineering standards, and best practices across data engineering, ML, GenAI, analytics, and platform capabilities. 

• Partner with business stakeholders, product owners, and cross-functional teams to translate manufacturing challenges into secure, scalable, production-ready AI and data solutions. 

• Design, develop, andmaintaincomplex ETL/ELT pipelines in Databricks usingPySpark, Scala, and SQL for large-scale structured and unstructured data processing. 

• Build efficient ingestion, transformation, migration, and deployment pipelines across databases, APIs, logs, event streams, images, PDFs, documents, and third-party platforms. 

• Design and implement GenAI solutions including RAG, embeddings, vector databases, agentic workflows, tool-calling systems, LLM orchestration, serving optimization, knowledge graphs, and metadata-driven retrieval. 

• Build GenAI applications using frameworks and platforms such asLangChain,LangGraph,LlamaIndex,DSPy, OpenAI APIs, Amazon Bedrock, or equivalent technologies. 

• Develop evaluation and observability frameworks tomonitormodel quality, hallucination rates, drift, retrieval effectiveness, latency, token usage, cost, reliability, operational health, and business impact. 

• Build and maintainMLOpsandLLMOpscapabilities, including experiment tracking, model registry, prompt management, versioning, CI/CD, automated testing, deployment automation, monitoring, governance, and release controls. 

• Design scalable data quality, validation, security, privacy, access control, logging, governance, and interoperability capabilities across hybrid cloud environments. 

• Automate manual processes, develop reusable frameworks and accelerators, and continuously improve engineering productivity, system reliability, and delivery efficiency. 

• Work in Agile/SAFeenvironments using JIRA, Confluence, and Agile DevOps tools to manage delivery, documentation, backlogs, user stories, and engineering execution.

Must-HaveSkills

• Hands-on experience with AWS, Databricks, Apache Spark,PySpark,SparkSQL, Python, and SQL for large-scale data engineering. 

• Strongproficiencyin workflow orchestration, Spark performance tuning, and scalable batch and streaming data pipeline development. 

• Experience with real-time data processing and integration using Apache Kafka,Debezium, or similar streaming technologies. 

• Hands-on experience withMLOpstools and practices, includingMLflow, model serving, feature stores, experiment tracking, deployment, and lifecycle management. 

• Experience with GenAI engineering practices, including prompt engineering, LLM evaluation, AI observability, agentic workflows, and knowledge graphs. 

• Ability to design and develop APIs or service interfaces for data, ML, and GenAI application integration. 

• Experience with Agile/SAFedelivery models, DevOps practices, CI/CD concepts, and cross-functional team collaboration. 

• Strong analytical, problem-solving, debugging, communication, and teamwork skills. 

• Ability to quickly learn, adapt, and apply emerging technologies across data, ML, and AI engineering.

Good-to-HaveSkills

• Experience with data engineering, analytics, ML, or AI solutions in pharma, biotech, manufacturing, or other regulated industries. 

• Familiarity with manufacturing systems and industrial data sources such as SCADA, Data Historian, MES, ERP, LIMS, or related platforms. 

• Experience with SQL, NoSQL, vector databases, knowledge graphs, data modeling, and OLAP/OLTP performance tuning. 

• Experience with Scala for Spark-based data engineering and distributed data processing. 

• Experience with Kubernetes or container orchestration platforms for scalable deployment, model serving, and production operations. 

• Experience applying software engineering best practices, including Git, automated testing, CI/CD, code reviews, and DevOps practices. 

• Experience using AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, or similar tools. 

• Experience collaborating with ML engineers, prompt engineers, product managers, product owners, architects, and business stakeholders on AI and data initiatives.

• Experience designing APIs or service interfaces to expose data, ML, or GenAI capabilities to consumers. 

Education and Professional Certifications

• Doctorate degree / Master's degree / Bachelor's degree and 8 to 13 years of experience years of experience in Computer Science, IT or related field 

SoftSkills

• Strong analytical, troubleshooting, and systems-thinking skills for solving complex data, ML, AI, and platform challenges. 

• Excellent verbal and written communication skills, with the ability to manage stakeholders and collaborate across global, virtual, cross-functional teams. 

• Strong ownership mindset with the ability to drive architecture decisions, manage competing priorities, and deliver outcomes independently. 

• Ability to mentor engineers, promote best practices, and support technical growth across the team. 

• Product-oriented mindset with the ability to connect technical solutions to user needs, business value, scalability, and operational impact. 

• Highly organized, detail-oriented, adaptable, and able to quickly learn and applynew technologies.