Director of AI Engineering
Full Time
-
The Director of AI Engineering will lead the development and deployment of production-grade AI/ML platforms across VGS biometrics, with a roadmap to expand capabilities into adjacent functions. This role will focus on translating AI and automation concepts into scalable, FDA-compliant, and revenue-generating solutions, beginning with SDTM and biometrics automation and extending to end-to-end clinical reporting and data pipelines. The role will own both the technical direction and delivery of these capabilities, moving initiatives from concept through production.
Key Responsibilities
• Define and drive the AI roadmap across biometrics (SDTM, ADaM, TFL) and Medical Writing
• Translate domain workflows into practical, AI-enabled systems (LLMs, RAG, agentic pipelines
• Partner closely with Programming, Biostats, and Data Management SMEs
• Lead biometrics automation efforts from concept/POC to production-grade platforms
• Build multi-tenant, cloud-native, API-first systems
• Ensure auditability, traceability, and regulatory compliance (GxP, 21 CFR Part 11)
• Build and scale an AI engineering team (initial 3–6, scaling to 10–20+), including mentoring and, where appropriate, managing through leads
• Establish MLOps, model evaluation, and deployment standards
• Drive engineering best practices, CI/CD, and system reliability
• Design systems for:
LLM pipelines (RAG, knowledge distillation)
Agent orchestration frameworks
Data transformation workflows (SAS/R integration)
• Own cloud strategy (Azure/AWS/GCP)
• Partner with BD to support RFPs/RFIs with AI capabilities
• Participate in client discussions, demos, and solution positioning
• Translate AI capabilities into practical, revenue-generating offerings
• Implement human-in-the-loop workflows
• Ensure audit trails, validation, and regulatory readiness
• Align outputs with CDISC and regulatory expectations
Required Qualifications
• 10–15+ years in software/AI engineering
• 3–5+ years leading engineering teams, including delivery ownership
• Proven experience building and deploying production AI/ML or data platforms (not just prototypes)
• Strong expertise in:
LLMs, RAG, agentic systems
Cloud platforms (AWS/Azure/GCP)
MLOps and scalable deployment
• Experience operating in regulated environments (GxP preferred)
Preferred
• Life sciences experience / clinical data exposure (SDTM, ADaM, CDISC)
• Experience building internal platforms or enterprise SaaS solutions
• Exposure to PHUSE / PharmaSUG ecosystem
Success Metrics (First 12 Months)
• SDTM or biometrics automation capability deployed and used internally
• Measurable reduction (target 30–50%) in SDTM programming effort
• 2–3 AI-enabled deals influenced or supported
• AI engineering team established and operating effectively
• Platform maturity sufficient for controlled client-facing demonstrations
The Director of AI Engineering will lead the development and deployment of production-grade AI/ML platforms across VGS biometrics, with a roadmap to expand capabilities into adjacent functions. This role will focus on translating AI and automation concepts into scalable, FDA-compliant, and revenue-generating solutions, beginning with SDTM and biometrics automation and extending to end-to-end clinical reporting and data pipelines. The role will own both the technical direction and delivery of these capabilities, moving initiatives from concept through production.
Key Responsibilities
• Define and drive the AI roadmap across biometrics (SDTM, ADaM, TFL) and Medical Writing
• Translate domain workflows into practical, AI-enabled systems (LLMs, RAG, agentic pipelines
• Partner closely with Programming, Biostats, and Data Management SMEs
• Lead biometrics automation efforts from concept/POC to production-grade platforms
• Build multi-tenant, cloud-native, API-first systems
• Ensure auditability, traceability, and regulatory compliance (GxP, 21 CFR Part 11)
• Build and scale an AI engineering team (initial 3–6, scaling to 10–20+), including mentoring and, where appropriate, managing through leads
• Establish MLOps, model evaluation, and deployment standards
• Drive engineering best practices, CI/CD, and system reliability
• Design systems for:
LLM pipelines (RAG, knowledge distillation)
Agent orchestration frameworks
Data transformation workflows (SAS/R integration)
• Own cloud strategy (Azure/AWS/GCP)
• Partner with BD to support RFPs/RFIs with AI capabilities
• Participate in client discussions, demos, and solution positioning
• Translate AI capabilities into practical, revenue-generating offerings
• Implement human-in-the-loop workflows
• Ensure audit trails, validation, and regulatory readiness
• Align outputs with CDISC and regulatory expectations
Required Qualifications
• 10–15+ years in software/AI engineering
• 3–5+ years leading engineering teams, including delivery ownership
• Proven experience building and deploying production AI/ML or data platforms (not just prototypes)
• Strong expertise in:
LLMs, RAG, agentic systems
Cloud platforms (AWS/Azure/GCP)
MLOps and scalable deployment
• Experience operating in regulated environments (GxP preferred)
Preferred
• Life sciences experience / clinical data exposure (SDTM, ADaM, CDISC)
• Experience building internal platforms or enterprise SaaS solutions
• Exposure to PHUSE / PharmaSUG ecosystem
Success Metrics (First 12 Months)
• SDTM or biometrics automation capability deployed and used internally
• Measurable reduction (target 30–50%) in SDTM programming effort
• 2–3 AI-enabled deals influenced or supported
• AI engineering team established and operating effectively
• Platform maturity sufficient for controlled client-facing demonstrations
Key Responsibilities
• Define and drive the AI roadmap across biometrics (SDTM, ADaM, TFL) and Medical Writing
• Translate domain workflows into practical, AI-enabled systems (LLMs, RAG, agentic pipelines
• Partner closely with Programming, Biostats, and Data Management SMEs
• Lead biometrics automation efforts from concept/POC to production-grade platforms
• Build multi-tenant, cloud-native, API-first systems
• Ensure auditability, traceability, and regulatory compliance (GxP, 21 CFR Part 11)
• Build and scale an AI engineering team (initial 3–6, scaling to 10–20+), including mentoring and, where appropriate, managing through leads
• Establish MLOps, model evaluation, and deployment standards
• Drive engineering best practices, CI/CD, and system reliability
• Design systems for:
LLM pipelines (RAG, knowledge distillation)
Agent orchestration frameworks
Data transformation workflows (SAS/R integration)
• Own cloud strategy (Azure/AWS/GCP)
• Partner with BD to support RFPs/RFIs with AI capabilities
• Participate in client discussions, demos, and solution positioning
• Translate AI capabilities into practical, revenue-generating offerings
• Implement human-in-the-loop workflows
• Ensure audit trails, validation, and regulatory readiness
• Align outputs with CDISC and regulatory expectations
Required Qualifications
• 10–15+ years in software/AI engineering
• 3–5+ years leading engineering teams, including delivery ownership
• Proven experience building and deploying production AI/ML or data platforms (not just prototypes)
• Strong expertise in:
LLMs, RAG, agentic systems
Cloud platforms (AWS/Azure/GCP)
MLOps and scalable deployment
• Experience operating in regulated environments (GxP preferred)
Preferred
• Life sciences experience / clinical data exposure (SDTM, ADaM, CDISC)
• Experience building internal platforms or enterprise SaaS solutions
• Exposure to PHUSE / PharmaSUG ecosystem
Success Metrics (First 12 Months)
• SDTM or biometrics automation capability deployed and used internally
• Measurable reduction (target 30–50%) in SDTM programming effort
• 2–3 AI-enabled deals influenced or supported
• AI engineering team established and operating effectively
• Platform maturity sufficient for controlled client-facing demonstrations
The Director of AI Engineering will lead the development and deployment of production-grade AI/ML platforms across VGS biometrics, with a roadmap to expand capabilities into adjacent functions. This role will focus on translating AI and automation concepts into scalable, FDA-compliant, and revenue-generating solutions, beginning with SDTM and biometrics automation and extending to end-to-end clinical reporting and data pipelines. The role will own both the technical direction and delivery of these capabilities, moving initiatives from concept through production.
Key Responsibilities
• Define and drive the AI roadmap across biometrics (SDTM, ADaM, TFL) and Medical Writing
• Translate domain workflows into practical, AI-enabled systems (LLMs, RAG, agentic pipelines
• Partner closely with Programming, Biostats, and Data Management SMEs
• Lead biometrics automation efforts from concept/POC to production-grade platforms
• Build multi-tenant, cloud-native, API-first systems
• Ensure auditability, traceability, and regulatory compliance (GxP, 21 CFR Part 11)
• Build and scale an AI engineering team (initial 3–6, scaling to 10–20+), including mentoring and, where appropriate, managing through leads
• Establish MLOps, model evaluation, and deployment standards
• Drive engineering best practices, CI/CD, and system reliability
• Design systems for:
LLM pipelines (RAG, knowledge distillation)
Agent orchestration frameworks
Data transformation workflows (SAS/R integration)
• Own cloud strategy (Azure/AWS/GCP)
• Partner with BD to support RFPs/RFIs with AI capabilities
• Participate in client discussions, demos, and solution positioning
• Translate AI capabilities into practical, revenue-generating offerings
• Implement human-in-the-loop workflows
• Ensure audit trails, validation, and regulatory readiness
• Align outputs with CDISC and regulatory expectations
Required Qualifications
• 10–15+ years in software/AI engineering
• 3–5+ years leading engineering teams, including delivery ownership
• Proven experience building and deploying production AI/ML or data platforms (not just prototypes)
• Strong expertise in:
LLMs, RAG, agentic systems
Cloud platforms (AWS/Azure/GCP)
MLOps and scalable deployment
• Experience operating in regulated environments (GxP preferred)
Preferred
• Life sciences experience / clinical data exposure (SDTM, ADaM, CDISC)
• Experience building internal platforms or enterprise SaaS solutions
• Exposure to PHUSE / PharmaSUG ecosystem
Success Metrics (First 12 Months)
• SDTM or biometrics automation capability deployed and used internally
• Measurable reduction (target 30–50%) in SDTM programming effort
• 2–3 AI-enabled deals influenced or supported
• AI engineering team established and operating effectively
• Platform maturity sufficient for controlled client-facing demonstrations
Motion Recruitment Partners (MRP) is an Equal Opportunity Employer. All applicants must be currently authorized to work on a full-time basis in the country for which they are applying, and no sponsorship is currently available. Employment is subject to the successful completion of a pre-employment screening. Accommodation will be provided in all parts of the hiring process as required under MRP’s Employment Accommodation policy. Applicants need to make their needs known in advance.