Principal Software Engineer

What you will do

Lead the end-to-end design, development, and delivery of machine learning and Generative AI (GenAI) solutions, from problem framing to production deployment and business impact realization.

Act as the technical owner for large-scale ML/GenAI initiatives, driving architecture decisions, scalability, reliability, and long-term maintainability.

Design and implement advanced agentic AI systems, including multi-agent architectures, reasoning workflows, tool integration, and autonomous decision-making systems.

Define and institutionalize evaluation, validation, and governance frameworks for ML/GenAI systems, including model performance, prompt evaluation, safety guardrails, hallucination mitigation, and compliance.

Partner directly with business stakeholders and product leaders to understand objectives, translate them into AI/ML solutions, and ensure measurable value delivery.

Establish and enforce best practices in MLOps, LLMOps, and DevOps, including CI/CD, monitoring, observability, reproducibility, and cost optimization.

Architect and oversee scalable cloud-based ML/GenAI platforms leveraging AWS, GCP, or Azure.

Drive experimentation strategy, including A/B testing, prompt optimization, and iterative improvement of models and agent workflows.

Provide technical leadership and mentorship to 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 of new technologies and approaches.

Design, develop, and implement robust data architectures and platforms to support ML Operation.

Ensure data integrity, accuracy, and consistency through rigorous quality checks and monitoring.

What we expect of you

• Deep expertise in machine learning, deep learning, and Generative AI (LLMs, transformers, embeddings, fine-tuning techniques).
• Proven track record of leading and delivering production-grade ML/GenAI systems end-to-end with measurable business impact with strong experience in designing scalable system architectures for ML and GenAI, including distributed systems and high-throughput pipelines.
• Expertise in MLOps/LLMOps ecosystems (MLflow, Kubeflow, Airflow, CI/CD, Docker, Kubernetes).
• Strong system design, architecture, and problem-solving skills with the ability to operate independently and lead large initiatives.
• Demonstrated proficiency in leveraging cloud platforms (AWS, Azure, GCP) for data engineering solutions. Strong understanding of cloud architecture principles and cost optimization strategies.
• Proven ability to mentor and guide junior and mid-level engineers (L4/L5).

Good-to-Have Skills

• Cloud Computing certificate preferred
• Experience with big data ecosystems (Spark, Hadoop) and large-scale data processing.
• Strong background in data engineering and building scalable data platforms.
• Advanced proficiency in Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, LangChain or 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.
• Knowledge of advanced statistical modeling, experimentation design, and causal inference.
• Experience with NLP, semantic search, embeddings, and vector search systems.
• Familiarity with Responsible AI practices, including fairness, explainability, governance, and regulatory considerations.
• Experience with cloud-native AI/ML services (AWS, Azure, GCP) and cost/performance optimization.
• Experience with Databricks platform for enterprise-scale ML and GenAI workloads.
• Exposure to advanced evaluation techniques, including red-teaming, adversarial testing, and synthetic data generation.
• Experienced with data modeling and performance tuning for both OLAP and OLTP databases.
• Experienced with Apache Spark, Apache Airflow and Databricks platform.

What you can expect of us

As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.

The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.

In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include:

• A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
• A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
• Stock-based long-term incentives
• Award-winning time-off plans
• Flexible work models where possible. Refer to the Work Location Type in the job posting to see if this applies.