Senior Principal AI Engineer
Vertex Pharmaceuticals
August 22, 2026
Remote friendly (Boston, MA)
United States
IT
Key Responsibilities:
- Define and build an enterprise agentic AI platform with shared architecture, services, and reusable capabilities.
- Operate a centralized AI Gateway/Control Plane/Control Tower for agent monitoring, observability, telemetry, policy enforcement, governance, guardrails, usage analytics, and auditability.
- Own strategy, architecture, implementation, and day-to-day operations for model access, routing, governance, cost management, and operational oversight.
- Implement intelligent model routing (provider abstraction, fallback/failover), rate limiting, and workload optimization across approved models.
- Lead AI FinOps (token/consumption visibility, budgeting, chargeback/showback, cost allocation/forecasting, model-cost optimization).
- Build centralized guardrails for content safety, prompt-injection defense, data-loss prevention, sensitive-data handling, and responsible AI policy enforcement.
- Deliver identity/access/security/privacy/regulatory compliance and lifecycle governance controls for models, agents, tools, and AI interactions.
- Provide end-to-end observability (quality monitoring, latency/reliability metrics, incident response, operational health).
- Manage usage analytics, immutable audit trails, policy evidence, risk reporting, and executive transparency.
- Lead RAG pipelines, retrieval/knowledge systems (vector search, document retrieval), agent lifecycle management (deployment/versioning/evaluation/reliability), and reusable platform services.
Required Qualifications:
- Advanced degree (or equivalent experience); 10+ years building enterprise AI/ML platforms and distributed systems.
- Deep expertise in agentic AI/LLM platform engineering; strong RAG and orchestration framework experience.
- Experience building centralized operational platforms for monitoring/governance/observability/control.
- Experience with autonomous coding agents and AI-assisted software development to design/test/debug/optimize/refactor complex systems.
- Strong evaluation/quality/performance optimization; cross-functional leadership and communication; balance strategy with hands-on execution.
Technical Skills Required (highlights): LLMs; agentic AI platform architecture; RAG; orchestration/workflow design; monitoring/observability; governance/policies; FinOps; AI Gateway/Control Tower; evaluation frameworks; platform/distributed systems/API and service design; knowledge retrieval systems; telemetry/logging/analytics; scalability/reliability/security.
Preferred Skills (optional): regulated environments; human-in-the-loop; policy engines/guardrails/audit frameworks; ML infrastructure/model serving; cloud-native/DevOps/MLOps; UX considerations; enterprise adoption strategy; leading platform transformation.
Application Instructions: Not specified.
- Define and build an enterprise agentic AI platform with shared architecture, services, and reusable capabilities.
- Operate a centralized AI Gateway/Control Plane/Control Tower for agent monitoring, observability, telemetry, policy enforcement, governance, guardrails, usage analytics, and auditability.
- Own strategy, architecture, implementation, and day-to-day operations for model access, routing, governance, cost management, and operational oversight.
- Implement intelligent model routing (provider abstraction, fallback/failover), rate limiting, and workload optimization across approved models.
- Lead AI FinOps (token/consumption visibility, budgeting, chargeback/showback, cost allocation/forecasting, model-cost optimization).
- Build centralized guardrails for content safety, prompt-injection defense, data-loss prevention, sensitive-data handling, and responsible AI policy enforcement.
- Deliver identity/access/security/privacy/regulatory compliance and lifecycle governance controls for models, agents, tools, and AI interactions.
- Provide end-to-end observability (quality monitoring, latency/reliability metrics, incident response, operational health).
- Manage usage analytics, immutable audit trails, policy evidence, risk reporting, and executive transparency.
- Lead RAG pipelines, retrieval/knowledge systems (vector search, document retrieval), agent lifecycle management (deployment/versioning/evaluation/reliability), and reusable platform services.
Required Qualifications:
- Advanced degree (or equivalent experience); 10+ years building enterprise AI/ML platforms and distributed systems.
- Deep expertise in agentic AI/LLM platform engineering; strong RAG and orchestration framework experience.
- Experience building centralized operational platforms for monitoring/governance/observability/control.
- Experience with autonomous coding agents and AI-assisted software development to design/test/debug/optimize/refactor complex systems.
- Strong evaluation/quality/performance optimization; cross-functional leadership and communication; balance strategy with hands-on execution.
Technical Skills Required (highlights): LLMs; agentic AI platform architecture; RAG; orchestration/workflow design; monitoring/observability; governance/policies; FinOps; AI Gateway/Control Tower; evaluation frameworks; platform/distributed systems/API and service design; knowledge retrieval systems; telemetry/logging/analytics; scalability/reliability/security.
Preferred Skills (optional): regulated environments; human-in-the-loop; policy engines/guardrails/audit frameworks; ML infrastructure/model serving; cloud-native/DevOps/MLOps; UX considerations; enterprise adoption strategy; leading platform transformation.
Application Instructions: Not specified.