About this role
Summary: Leads AI and ML engineering efforts by identifying business problems suitable for AI solutions and building agentic AI prototypes and pilots. Establishes reusable AI assets and communicates findings to senior leaders to inform scale decisions.
Role summary
Work with a team of engineers and cross-divisionally with our partners to identify and define business problems suitable for artificial Intelligence.Actively participate in developing strategy around which frameworks, technologies and infrastructure our team should use in our proof-of-concepts and pilots for AI solutions.Lead and worked on team building proof-of-concepts and prototypes that can be used to demonstrate the impact of Artificial Intelligence in Delta's various business units
Key responsibilities:
- Partner cross-divisionally with business, product, architecture, data, cybersecurity, privacy, legal, risk and operations stakeholders to identify, prioritize and define business problems suitable for AI, GenAI and Agentic AI solutions
- Architect and build agentic experiences using enterprise-ready patterns such as single-agent patterns, multi-agent orchestration, tool/function calling, RAG, memory, policy-aware context management, human-in-the-loop escalation and reusable agent skills/workflows
- Evaluate and apply modern AI engineering tools and Agentic AI IDEs/coding assistants to accelerate design, prototyping, testing and delivery while maintaining quality, security and compliance expectations
- Drive hands-on prototypes, pilots and proofs of concepts that validate business value, user experience, technical feasibility, cost profile, safety posture and operational readiness
- Establish reusable AI assets, including prompts, evaluation datasets, guardrail patterns, MCP-enabled tool integrations, RAG pipelines, observability dashboards, reference architectures and implementation playbooks
- Define and influence technical standards for agent development
- Embed responsible AI principles, data privacy, model risk awareness, explainability, human oversight, secure-by-design practices and enterprise governance controls into every stage of experimentation and delivery
- Communicate findings clearly to senior leaders and enterprise partners through demos, technical readouts, decision memos, scorecards and adoption recommendations that help inform broader scale decisions
- Stay current on emerging GenAI, Agentic AI, ML platforms, model capabilities, orchestration frameworks, governance practices and AI safety/security threats; translate market learning into pragmatic recommendations
- Strong foundation in Machine Learning concepts and techniques, including model development, training, evaluation and optimization, with practical experience in areas such as classification, regression, NLP, computer vision, forecasting or recommendation systems