> Markdown version of [/jobs/ext/2039069-principal-software-engineer](https://www.wearedevelopers.com/jobs/ext/2039069-principal-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Software Engineer - **Company:** Amgen - **Location:** Tampa, FL, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Cloud Engineering, Code Review, Continuous Integration, Data Architecture, Information Engineering, Data Integrity, DevOps, Distributed Computing Environment, Distributed Systems, Apache Hadoop, Python (Programming Language), Machine Learning, Tensorflow, Search Technologies, Software Deployment, Google Cloud, Pytorch, Large Language Models, Multi-Agent Systems, Apache Spark, Deep Learning, Generative AI, Kubernetes, Information Technology, HuggingFace, Data Management, Machine Learning Operations, Virtual Agents, Data Pipelines, Docker - **Published:** August 12, 2026 - **Apply:** https://www.biospace.com/logon?PipelinedPage=%2Fjob%2F3067987%2Fprincipal-software-engineer%3FAction%3DContinueJobApplication%23application-form ## About the Role Doctorate degree and 2 years ofexperience OR Masters degree and 4 years of experience OR Bachelors degree and 6 years of experience OR Associates degree and 10 years of experience OR High school diploma / GED and 12 years of experience * Deepexpertisein machine learning, deep learning, and Generative AI (LLMs, transformers, embeddings, fine-tuning techniques). * Proventrack recordofleading and delivering production-grade ML/GenAI systems end-to-endwith measurable business impactwith strong experience in designingscalable system architecturesfor ML and GenAI, including distributed systems and high-throughput pipelines. * ExpertiseinMLOps/LLMOpsecosystems(MLflow, Kubeflow, Airflow, CI/CD, Docker, Kubernetes). * Strong systemdesign, architecture, and problem-solving skills with the ability tooperateindependently and lead large initiatives. * Demonstratedproficiencyinleveragingcloud platforms (AWS, Azure, GCP) for data engineering solutions. Strong understanding of cloud architecture principles and cost optimization strategies. * Provenability to mentor and guide junior and mid-level engineers (L4/L5). Good-to-Have Skills: Degree in computer science,Statistics,and Data Science preferred.Masters degree and 6+years experienceOrBachelors degree and 8+years experience * Cloud Computing certificate preferred Experiencewith big data ecosystems (Spark, Hadoop) and large-scale data processing. Strongbackground in data engineering and building scalable data platforms. * Advancedproficiencyin Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face,LangChainor similar). * Experience designing robust evaluation and validation systems, including automated evals, human-in-the-loop, safety testing, and monitoring frameworks. * Extensive experience with RAG architectures, vector databases, and knowledge-grounded systems. * Strong understanding of agentic AI frameworks, including orchestration, planning, memory, and tool use. Knowledgeof advanced statistical modeling, experimentation design, and causal inference. Experiencewith NLP, semantic search, embeddings, and vector search systems. ## Description Lets do this. Lets change the world. In this vital role you will play a pivotal role in building and scaling our machine learning models from development to production. Yourexpertisein both machine learning and operations will be essential in creating efficient and reliable ML pipelines.A background in data engineering, including experience with data pipelines and distributed data processing, is a strong plus. Roles & Responsibilities: * Lead theend-to-end design, development, and deliveryof machine learning and Generative AI (GenAI) solutions, from problem framing to production deployment and business impact realization. * Act as thetechnical ownerfor large-scale ML/GenAI initiatives, driving architecture decisions, scalability, reliability, and long-term maintainability. * Design and implement advancedagentic AI systems, including multi-agent architectures, reasoning workflows, tool integration, and autonomous decision-making systems. * Define and institutionalizeevaluation, validation, and governance frameworksfor ML/GenAI systems, including model performance, prompt evaluation, safety guardrails, hallucination mitigation, and compliance. * Partner directly withbusiness stakeholders and product leadersto understandobjectives, translate them into AI/ML solutions, and ensure measurable value delivery. * Establish and enforce best practices inMLOps,LLMOps, and DevOps, including CI/CD, monitoring, observability, reproducibility, and cost optimization. * Architect and overseescalable cloud-based ML/GenAI platformsleveragingAWS, GCP, or Azure. * Drive experimentation strategy, including A/B testing, prompt optimization, and iterative improvement of models and agent workflows. * Providetechnical leadership and mentorshipto L4 and L5 engineers, including design reviews, code reviews, and career guidance. * Lead cross-functional collaboration across data science, engineering, product, and business teams to deliver integrated AI solutions. * Stay at the forefront of advancements in machine learning, Generative AI, and agentic systems, and drive adoption ofnew technologiesand approaches. * Design, develop, and implement robust data architectures and platforms to supportML Operation. * Ensuring data integrity, accuracy, and consistency through rigorous quality checks and monitoring. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)