AI​/ML & Analytics Platform Engineer

As part of our rapidly growing AI & Analytics Platform Engineering & Standards team, you will play a pivotal role in building and scaling the core developer platform that powers AI/ML initiatives across R&D, Commercial, and functional business units. You will engineer secure, high‑performance, self‑service infrastructure that enables our teams to accelerate scientific discoveries and deliver real-world value.

If you are passionate about AI/ML platforms, automation, and developer experience—and want your work to directly support life‑changing science—this role is for you.

• Build and enhance the core AI/ML & Analytics platform, services, and tools across development, testing, and production environments.
• Develop platform capabilities to support scalable batch and real-time workflows, including low‑latency predictions and offline inference.
• Improve platform performance, automate operations, scale compute resources, and streamline deployment processes.
• Collaborate with foundational cloud teams to ensure platform reliability, security, efficiency, and operational excellence.
• Implement monitoring, observability, and governance frameworks, including registry systems, alerts, and compliance controls.
• Partner with cross-functional spoke teams to design AI/ML architecture, pipelines, and scalable deployment solutions.
• Champion self-service platform usage, IaC practices, and Git Ops methodologies to enhance developer experience.
• Continuously refine platform usability, automation, and end‑user efficiency.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, Operations Research, or related field; 5+ years of relevant experience.
• Proven experience building AI/ML & Analytics platforms for ML Researchers, Engineers, Data Scientists, or Analysts.
• Strong programming experience in Python, Spark, SQL, or similar languages; familiarity with ML frameworks such as PyTorch or Tensor Flow.
• Experience designing scalable self-service systems using microservices and/or event-driven architectures.
• Hands-on experience with AWS, particularly AI/ML-related services (e.g., Sage Maker).
• Proficiency with Infrastructure as Code (Terraform, Open Tofu, CDK, Pulumi, etc.) and CI/CD pipelines.
• Working knowledge of version control (Git Hub/Git Lab), CI/CD tools (Actions, Jenkins), and workflow systems (JIRA).
• Experience with containerization (Docker, Podman) and orchestration platforms (Kubernetes, Rancher).
• Exposure to large-scale CPU/GPU/multi‑GPU environments (CUDA fundamentals a plus).
• Understanding of operational capabilities, including observability, monitoring, tracking, and registries.
• Demonstrated ability to analyze performance, optimize systems, and manage cloud-scale cost efficiency.
• Strong communication skills with the ability to collaborate effectively across teams.
• Experience in the pharmaceutical or biotech industry.
• Proficiency in strongly typed languages such as C/C++, Java, Go, or Rust.
• Hands-on experience with distributed systems (Ray, Dask, Spark) or high-performance computing (Slurm).
• Familiarity with data platforms such as Databricks, Snowflake, or AWS Lake Formation, and technologies like Delta, Iceberg, Hudi.
• Experience building or operating real-time/streaming systems (Kafka, Spark Streaming).
• Knowledge of Git Ops-based tools such as ArgoCD, Crossplane.
• Experience working in multi-cloud environments (AWS, GCP, Azure).
• Familiarity with high‑performance inference/training frameworks such as ONNX Runtime, Tensor
  
  RT, or Triton.