Associate Director, AI & ML Ops Lead -- Kite Commercial
Gilead Sciences
August 17, 2026
On-site
Raleigh, NC
IT
Responsibilities
- Model Lifecycle Management: Develop and maintain pipelines to transition models from experimentation to production (packaging, CI/CD, automated testing, deployment) and support model serving for patient identification, alignment prediction, next-best-action engines, and competitive intelligence models.
- Data Pipeline Development: Design robust batch and streaming workflows; integrate/define/manage feature sets, lineage, and reuse.
- Production Operations & Monitoring: Ensure reliability/scalability; implement logging, tracing, alerting; monitor performance, data drift, bias, and service health; monitor data quality for rare disease feeds.
- Agent Workflow Development: Decompose complex workflows into agent-executable workstreams; define boundaries between agent execution and human judgment.
- Instruction Architecture & Prompt Engineering: Design prompt architectures, agent skills/memories/context patterns; author structured coding instructions with acceptance criteria.
- Build agentic AI systems to detect anomalies (e.g., competitive switching, patient discontinuation, payer access changes) and generate hypotheses/recommended actions to stakeholders and CRM.
- Token Economics & Cost Optimization: Optimize for cost efficiency (context, token usage) and monitor token economics per workstream.
- Governance, Security & Compliance: Implement version control/approvals/audit trails; ensure outputs are explainable/auditable and comply with HIPAA/PHI, GDPR, FDA promotional regulations, and REMS; enforce secrets management, RBAC, network policies, and data protection.
- Testing & Validation: Set up testing frameworks for ML models and agent-generated code; automated quality gates (type checking, linting, integration/contract tests).
- Documentation & Release Management: Create guides/operational playbooks/user instructions; coordinate releases; maintain runbooks/rollback/change tickets.
Qualifications
- Basic: Bachelor’s + 10 years OR Master’s + 8 years OR PhD + 2 years.
- Preferred: 3–6+ years in MLOps/Data Engineering/ML platform roles; 2+ years building complex analytics.
- Python and SQL; familiarity with TypeScript/JavaScript or Go/Rust; TDD, CI/CD, code quality.
- CI/CD (e.g., GitHub Actions), Docker, cloud infrastructure concepts.
- Model packaging/serving (SageMaker, Databricks MLflow), experiment tracking, model registry.
- Databricks (Spark), Airflow, MLflow.
- AI coding tools (Claude Code/GitHub Copilot/Cursor) and LLM serving platforms; prompt engineering discipline.
- Healthcare data privacy/security; secrets management, audit controls; HIPAA, SOC 2, 21 CFR Part 11.
- Systems thinking, pharmaceutical CGT/specialty pharma commercial analytics; CGT data (e.g., IQVIA, Veeva CRM, claims/RWD).
- Multi-agent workflow/orchestration experience; MCP and agent interoperability; scalability/performance (batch scoring, low-latency APIs, high-throughput inference).
- Enterprise integrations (Veeva, Salesforce, Microsoft 365, ServiceNow APIs).
- People leadership: inclusion, developing talent, empowering teams.
Benefits
- Salary range: $168,980.00 - $218,680.00; may be eligible for annual bonus, stock-based incentives, paid time off, and company-sponsored medical/dental/vision/life insurance.
Application Instructions
- For current employees/contractors: apply via the Internal Career Opportunities portal in Workday.
- Model Lifecycle Management: Develop and maintain pipelines to transition models from experimentation to production (packaging, CI/CD, automated testing, deployment) and support model serving for patient identification, alignment prediction, next-best-action engines, and competitive intelligence models.
- Data Pipeline Development: Design robust batch and streaming workflows; integrate/define/manage feature sets, lineage, and reuse.
- Production Operations & Monitoring: Ensure reliability/scalability; implement logging, tracing, alerting; monitor performance, data drift, bias, and service health; monitor data quality for rare disease feeds.
- Agent Workflow Development: Decompose complex workflows into agent-executable workstreams; define boundaries between agent execution and human judgment.
- Instruction Architecture & Prompt Engineering: Design prompt architectures, agent skills/memories/context patterns; author structured coding instructions with acceptance criteria.
- Build agentic AI systems to detect anomalies (e.g., competitive switching, patient discontinuation, payer access changes) and generate hypotheses/recommended actions to stakeholders and CRM.
- Token Economics & Cost Optimization: Optimize for cost efficiency (context, token usage) and monitor token economics per workstream.
- Governance, Security & Compliance: Implement version control/approvals/audit trails; ensure outputs are explainable/auditable and comply with HIPAA/PHI, GDPR, FDA promotional regulations, and REMS; enforce secrets management, RBAC, network policies, and data protection.
- Testing & Validation: Set up testing frameworks for ML models and agent-generated code; automated quality gates (type checking, linting, integration/contract tests).
- Documentation & Release Management: Create guides/operational playbooks/user instructions; coordinate releases; maintain runbooks/rollback/change tickets.
Qualifications
- Basic: Bachelor’s + 10 years OR Master’s + 8 years OR PhD + 2 years.
- Preferred: 3–6+ years in MLOps/Data Engineering/ML platform roles; 2+ years building complex analytics.
- Python and SQL; familiarity with TypeScript/JavaScript or Go/Rust; TDD, CI/CD, code quality.
- CI/CD (e.g., GitHub Actions), Docker, cloud infrastructure concepts.
- Model packaging/serving (SageMaker, Databricks MLflow), experiment tracking, model registry.
- Databricks (Spark), Airflow, MLflow.
- AI coding tools (Claude Code/GitHub Copilot/Cursor) and LLM serving platforms; prompt engineering discipline.
- Healthcare data privacy/security; secrets management, audit controls; HIPAA, SOC 2, 21 CFR Part 11.
- Systems thinking, pharmaceutical CGT/specialty pharma commercial analytics; CGT data (e.g., IQVIA, Veeva CRM, claims/RWD).
- Multi-agent workflow/orchestration experience; MCP and agent interoperability; scalability/performance (batch scoring, low-latency APIs, high-throughput inference).
- Enterprise integrations (Veeva, Salesforce, Microsoft 365, ServiceNow APIs).
- People leadership: inclusion, developing talent, empowering teams.
Benefits
- Salary range: $168,980.00 - $218,680.00; may be eligible for annual bonus, stock-based incentives, paid time off, and company-sponsored medical/dental/vision/life insurance.
Application Instructions
- For current employees/contractors: apply via the Internal Career Opportunities portal in Workday.