AI/ML & Analytics Platform Engineer

Key Responsibilities: Contribute to building AI/ML & Analytics platform, services, and tools across dev, test, and prod environments to accelerate model training, inference, and deployment. Build capabilities for batch and real-time workflows at scale with flexible deployment strategies for use cases like low-latency predictions and offline inference. Improve platform performance, reduce manual intervention, scale compute, and increase deployment efficiency. Collaborate with cloud teams to ensure operational effectiveness, reliability, security, and efficiency. Provide technical guidance on monitoring systems like registries and alerting, plus governance frameworks for regulatory compliance. Work with cross-functional teams on AI/ML system architecture, deployment pipelines, and solution scaling. Champion self-service patterns, IaC, and GitOps for platform development. Required

Technical Skills: Experience building scalable AI/ML & Analytics platforms for ML Researchers, Engineers, Data Scientists, and Analysts. Proficiency in Python, Spark, SQL, and ML frameworks like PyTorch or TensorFlow. Strong AWS knowledge, including AI/ML services like SageMaker. IaC tools such as Terraform, OpenTofu, CDK, or Pulumi, plus CI/CD pipelines. Containerization with Docker or Podman, and orchestration with Kubernetes or Rancher. VCS like GitHub or GitLab, CI/CD tools like GitHub Actions or Jenkins, and JIRA. Ops fundamentals including registries, observability, monitoring, performance analysis, and cost optimization.

Required: Functional/Behavioral Skills: Hands-on problem-solving for technical and architectural challenges in scalable, secure platforms. Automation-first mindset with security consciousness and focus on developer experience. Strong communication to engage stakeholders effectively. Ability to work collaboratively in cross-functional, agile teams valuing individual development. Preferred

Skills: Pharma/biotech domain experience. Strongly typed languages like C/C, Java, Go, or Rust. Large-scale distributed systems like Ray, Dask, Spark, or HPC like Slurm. Data platforms like Databricks, Snowflake, or dbt with Delta, Iceberg, Hudi. Real-time streaming like Kafka or Spark Streaming. GitOps tools like ArgoCD or Crossplane. Multi-cloud (AWS, GCP, Azure). High-performance inference frameworks like ONNX Runtime, TensorRT, or Triton. Large-scale CPU/GPU infrastructure with CUDA knowledge.