Principal Software Engineer

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21 hr. ago 7 hr. ago

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Amgen Technology Principal Software Engineer

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Job Summary

Join Amgen's Mission to Serve Patients

Principal Software Engineer

We are developing Manufacturing and Engineering AI tools that help employees gain insights, access knowledge, and make better decisions more efficiently. The Principal Software Engineer will set the technical direction and lead architecture for platform initiatives supporting chatbots, agents, and AI-enabled workflows across manufacturing and engineering use cases.

This role requires deep manufacturing technology fluency and hands-on experience with the platform stack behind industrial and enterprise AI solutions. You will guide architecture and standards for systems that integrate with manufacturing data sources, operational platforms, and modern GenAI services, while supporting both low/no-code and pro-code development patterns.

This is a hands-on technical leadership role. You will define architecture, evaluate build-versus-buy options, own reliability and cost characteristics, and lead a team of engineers working with product owners and principal engineers to evolve and operate these platforms.

Responsibilities

Define the technical vision and reference architecture for AI platforms supporting chatbots, agents, orchestration, and related enterprise services.
Translate product and business requirements into scalable platform capabilities, including agent hosting, model access, AI gateways, observability, and operational tooling.
Drive platform decisions around LLM serving, model endpoints, caching, batching, latency-versus-cost tradeoffs, and multi-model support.
Lead architecture for manufacturing integrations and industrial data connectivity, including patterns for SCADA, Data Historian, MES, ERP, LIMS, APIs, event streams, and document-based knowledge sources.
Own platform reliability, scalability, and cost by defining SLIs/SLOs, capacity planning, cost attribution, and FinOps practices.
Collaborate with Product Owners, Principal Engineers, and stakeholders to define roadmap, acceptance criteria, and delivery milestones.
Lead and mentor engineers delivering platform services, integrations, CI/CD for agents and models, and marketplace/catalog capabilities.
Establish standards for security, compliance, and model governance, including data handling, access controls, logging, auditability, and traceability.
Be hands-on when needed to prototype architectures, review designs, troubleshoot production incidents, and participate in code and design reviews.

Technology and domain experience

The ideal candidate will bring direct experience with manufacturing or industrial technology environments, along with strong fluency in modern AI and data platforms. Experience in one or more of the following areas is strongly preferred:

Manufacturing systems and industrial data sources such as SCADA, Data Historian, MES, ERP, and LIMS.
Cloud and data platforms including AWS, Databricks, Apache Spark, PySpark, Scala, Python, and SQL.
Real-time and event-driven integration using Apache Kafka, Debezium, or similar streaming technologies.
GenAI and MLOps capabilities such as MLflow, model serving, experiment tracking, prompt management, deployment automation, and governance.
AI application frameworks and orchestration tools such as LangChain, LangGraph, LlamaIndex, DSPy, Amazon Bedrock, and OpenAI APIs.
Knowledge retrieval patterns including RAG, embeddings, vector databases, knowledge graphs, and metadata-driven search.
Production engineering practices including Kubernetes, containers, microservices, API gateway patterns, CI/CD, automated testing, and observability.
Agile/SAFe delivery using JIRA, Confluence, and DevOps tooling.

Basic Qualifications

Master's / Bachelors degree in Computer Science, Engineering, or equivalent practical experience.
13-17 years of engineering experience building or platforming cloud services or developer platforms, with 3+ years leading engineering teams or technical programs.
Proven experience designing and operating cloud-native platforms using Kubernetes, containers, microservices, and related distributed system patterns.
Hands-on experience with LLM serving or adjacent model-serving patterns, including inference endpoints, routing, scaling, batching, and latency/cost optimization.
Practical knowledge of API gateway patterns, authentication and authorization, and secure integrations.
Familiarity with cost attribution and FinOps concepts for cloud and AI workloads.
Strong track record partnering with product managers and senior technical stakeholders to deliver platform capabilities and roadmaps.
Excellent communication skills with the ability to explain technical tradeoffs clearly to both technical and non-technical audiences.
Experience with observability and SRE practices, including metrics, tracing, logging, incident management, and production support.

Preferred Qualifications

Direct experience with manufacturing applications, industrial data platforms, or regulated manufacturing environments.
Experience with agentic AI platforms, agent hosting frameworks, and workflow orchestration tools.
Familiarity with the broader LLM ecosystem, including model providers, orchestration libraries, and governance frameworks.
Experience implementing AI gateways, centralized model access controls, policy layers, or audit systems.
Experience with FinOps tooling and cost-allocation models for platform services.
Experience shipping developer experience capabilities such as SDKs, CLIs, templates, and documentation.

Soft Skills

Excellent analytical and troubleshooting skills.
Strong verbal and written communication skills.

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