Principal Architect
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Job Description
Principal Architect implementing machine learning and Generative AI solutions at Amgen. Leading design and development of efficient ML pipelines from development to production.
Responsibilities:
- Play a pivotal role in building and scaling machine learning models from development to production.
- Create efficient and reliable ML pipelines.
- Lead the end-to-end design, development, and delivery of ML and GenAI solutions.
- Design and implement advanced data pipelines and AI systems, including batch and streaming data processing.
- Build and optimize robust data foundations for AI by developing high-quality, scalable ETL/ELT pipelines.
- Define and institutionalize evaluation, validation, and governance frameworks for ML/GenAI systems.
- Partner with business stakeholders to translate objectives into data-driven AI/ML solutions.
- Establish and enforce best practices in MLOps, LLMOps, DataOps, and DevOps.
- Architect and oversee scalable cloud-based data and AI platforms.
- Drive experimentation strategy, including A/B testing and prompt optimization.
- Provide mentorship to L4 and L5 engineers in data engineering and AI/ML development.
- Lead cross-functional collaboration across data engineering, data science, platform engineering, and business teams to deliver integrated AI solutions.
- Stay at the forefront of advancements in data engineering and Generative AI.
Requirements:
- Doctorate degree and 2 years of experience OR Master’s degree and 4 years of experience OR Bachelor’s degree and 6 years of experience OR Associate’s degree and 10 years of experience OR High school diploma / GED and 12 years of experience
- 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 certifications (AWS, Azure, or GCP) are a plus
- Strong experience with big data ecosystems , including Apache Spark, Hadoop , and large-scale distributed data processing
- Deep expertise in data engineering , including building and optimizing scalable data pipelines and platforms using Databricks, Spark, SQL, and Python
- Advanced proficiency in Python and modern ML/AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face, LangChain, or similar)
- Experience designing robust evaluation and validation frameworks , including automated evaluations, human-in-the-loop systems, safety testing, and monitoring
- Extensive experience with Retrieval-Augmented Generation (RAG) architectures, vector databases , and knowledge-grounded AI systems
- Strong understanding of agentic AI frameworks , including orchestration, planning, memory management, and tool integration
- Solid foundation in statistical modeling, experimentation design (A/B testing), and causal inference
- Experience with NLP, semantic search, embeddings, and vector search systems
- Familiarity with Responsible AI practices , including fairness, explainability, governance, and regulatory compliance
- Hands-on experience with cloud-native AI/ML services across AWS, Azure, or GCP , including cost and performance optimization
- Experience with the Databricks Lakehouse platform for enterprise-scale data engineering, ML, and GenAI workloads
- Exposure to advanced evaluation techniques such as red-teaming, adversarial testing, and synthetic data generation
- Strong experience in data modeling and performance tuning for both OLAP and OLTP systems
- Hands-on experience with workflow orchestration tools such as Apache Airflow , and distributed processing frameworks like Apache Spark
Benefits:
- 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
- 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.
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