Data Engineering Professional II

We are seeking an MLOps Engineer to operationalize machine learning and generative AI across our R&D and enterprise data ecosystem. You will build and maintain the platforms, pipelines, and controls that move models from notebook experiments into validated, production-grade, GxP-compliant services: supporting use cases that span clinical development, regulatory operations, pharmacovigilance, real-world evidence, and translational/biomarker research.

This is a hands-on engineering role at the intersection of data engineering, ML lifecycle automation, and regulated-systems discipline.

You will work primarily in Databricks and AWS, partnering with data scientists, platform/cloud engineering, quality, and regulatory teams to ship models that are reproducible, monitored, auditable, and trustworthy.

Design, build, and operate end-to-end ML pipelines (data ingestion → feature engineering → training → validation → deployment → monitoring) using Databricks (Delta Lake, MLflow, Unity Catalog, Feature Store, Workflows/Jobs) and AWS services.

Implement CI/CD for ML and data assets (e.g., GitHub Actions, GitLab CI, or Jenkins), including automated testing, environment promotion (dev → test → prod), and reproducible builds.

Stand up and maintain model registries, model versioning, and artifact lineage so every deployed model is traceable to its data, code, and configuration.

Build and manage ML infrastructure on AWS — e.g., SageMaker, Bedrock, S3, Lambda, ECS/EKS, Step Functions, ECR, IAM, CloudWatch — using Infrastructure as Code (Terraform or CloudFormation/CDK).

Integrate Databricks with AWS securely (Unity Catalog governance, cross-account access, VPC/networking, KMS encryption, secrets management).

Optimize compute and cost (cluster policies, autoscaling, spot strategy, job orchestration) without compromising performance or compliance.

Implement model and data monitoring: drift detection, data-quality checks, performance/SLA tracking, and automated alerting/retraining triggers.

Establish observability and incident-response practices for ML services; participate in on-call/runbook ownership as needed.

Maintain feature stores and data contracts to ensure consistency between training and serving.

Build ML systems that meet GxP expectations and support Computer System Validation (CSV) / Computer Software Assurance (CSA), GAMP 5, 21 CFR Part 11, and data-integrity (ALCOA+) requirements.

Implement audit trails, electronic records/signatures controls, access controls, and change-management workflows suitable for validated environments.

Handle PII/PHI and sensitive R&D data in line with HIPAA, GDPR, and internal privacy/data-governance policies (de-identification, anonymization, role-based access).

Author and maintain technical documentation, validation deliverables, and SOP-aligned procedures; partner with Quality/QA and Regulatory on audits and inspections.

Work under the guidance of Director, Solution Engineering/Solution Architect to produce artifacts and deliverables that adhere to best practices at Takeda.

Partner with data scientists to productionize models (including LLM/GenAI and RAG applications) and to translate research code into robust, maintainable services.

Contribute reusable templates, accelerators, and self-service tooling that raise the engineering bar across teams.

Promote MLOps best practices, mentor peers, and document standards.

Production ML/GenAI pipelines run reproducibly with full lineage, monitoring, and automated promotion across validated environments.

Deployment lead time and manual handoffs are measurably reduced through reusable templates and CI/CD.

Models in production are monitored for drift and quality, with documented retraining and rollback procedures.

Engineering artifacts meet inspection-readiness standards and pass internal QA review.