Dir AI/ML Engineering
Optum- Posted 54 minutes ago
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Job Description
We are seeking an experienced Director, AI/ML Engineering to lead the design, development, and delivery of next-generation AI-native products and Agentic AI solutions. This role requires a hands-on technology leader with 15+ years of software engineering experience and deep expertise in Artificial Intelligence, Machine Learning, Large Language Models (LLMs), and Agentic AI platforms.
The ideal candidate will have a proven track record of building and scaling AI-powered products, leading multiple Agile/Scrum teams, and driving enterprise-wide AI transformation initiatives. You will work closely with business executives, product leaders, architects, and engineering teams to define AI strategy, deliver innovative solutions, and ensure successful execution through AI-Driven Software Development Lifecycle (AIDLC) practices.
This leader must be equally comfortable discussing executive-level business strategy and diving deep into system architecture, model development, orchestration frameworks, MCP-based integrations, AI agents, and production-scale deployment. Healthcare domain experience is highly desirable, particularly in developing responsible, scalable, and compliant AI solutions that improve business and customer outcomes.
Key Responsibilities
- Lead the strategy, architecture, development, and delivery of AI-native products and platforms.
- Design and implement Agentic AI solutions leveraging LLMs, multi-agent systems, orchestration frameworks, MCP servers, and enterprise data platforms.
- Provide hands-on technical leadership in AI/ML model development, deployment, evaluation, and continuous improvement.
- Manage and mentor multiple Scrum teams while fostering a high-performance engineering culture.
- Drive execution using AIDLC methodologies and modern software engineering best practices.
- Partner with business stakeholders and executive leadership to align AI initiatives with organizational goals and measurable outcomes.
- Establish engineering standards for Responsible AI, security, scalability, governance, and operational excellence.
- Lead product roadmaps, delivery planning, resource management, and organizational capability development.
- Ensure successful production deployment and operationalization of AI solutions with measurable business impact.
- Stay current with emerging AI technologies and drive innovation across the enterprise.
Required Qualifications
- 15+ years of software engineering and technology leadership experience.
- 4+ years of hands-on experience developing and deploying Agentic AI solutions.
- Strong expertise in AI/ML, Generative AI, LLMs, RAG, orchestration frameworks, and AI agents.
- Demonstrated success building and scaling AI-native products from concept to production.
- Experience leading multiple Agile/Scrum teams and large-scale engineering programs.
- Strong stakeholder management skills with experience presenting to executive leadership.
- Deep understanding of Software Development Life Cycle (SDLC) and AI-Driven Life Cycle (AIDLC) methodologies.
- Hands-on proficiency in AI application development, architecture, cloud platforms, APIs, and enterprise integration patterns.
- Excellent leadership, organizational, communication, and people-management skills.
Preferred Qualifications
- Healthcare or HealthTech industry experience.
- Experience with clinical, claims, provider, member, or population health data.
- Familiarity with Responsible AI, governance, security, privacy, and regulatory compliance.
- Experience building enterprise AI platforms, clinical AI solutions, or workflow automation systems.
Success Profile
A visionary yet hands-on leader who can transform business challenges into scalable AI solutions, inspire engineering teams, influence executives, and accelerate enterprise adoption of AI through innovation, technical excellence, and disciplined execution.
More Info
Key Skills
Generative AI
MCP servers
Cloud platforms
Responsible AI governance
AI ML model development
AI-Driven Software Development Lifecycle (AIDLC)
Enterprise integration patterns
RAG orchestration frameworks
Multi-agent systems orchestration frameworks
Software Development Life Cycle (SDLC)
Agentic AI platforms
AI agents
Large Language Models (LLMs)
