Distinguished AI Engineer

Elevance Health•Richmond, VA

17h•Hybrid

About The Position

The Distinguished AI Engineer is a highly experienced, hands-on AI individual contributor to design, build, and scale enterprise AI solutions across machine learning, generative AI, and AI agents. This role is for a senior technical expert who can directly develop production-grade AI capabilities while also influencing architecture, delivery models, and engineering practices across the organization. The ideal candidate combines deep technical expertise with strong business acumen and has a proven track record of building reusable AI services at scale. This is not a pure research or management role; we are looking for a hands-on builder who thrives in complex enterprise environments and can help modernize how AI solutions are developed and deployed. The ideal candidate can also make sound buy, build, and integrate decisions across commercial and open-source AI solutions, selecting the right approach based on business need, regulatory requirements, speed to value, and long-term maintainability.

Requirements

Requires a bachelor's degree in Information Technology, Computer Science, Engineering, or a related field and a minimum of 15 years of experience in software engineering, including experience leading, designing, implementing, and operating distributed systems and large-scale architectures, or any combination of education and experience which would provide an equivalent background.
Experience designing, developing, and maintaining software solutions using Python, Java, or comparable object-oriented programming languages also required, as well as experience designing and implementing APIs, microservices, and event-driven architectures, and applying cloud, CI/CD, and DevOps practices.

Nice To Haves

Subject matter expert in machine learning, GenAI, and AI agents.
Experience working in a Fortune 100 regulated company, startup, and or top-tier technology firm, ideally in combination.
Proven success at building and deploying reusable AI services at scale
Experience operationalizing AI in production with MLOps, LLMOps, observability, and governance practices.

Responsibilities

Design, develop, and deploy enterprise AI solutions spanning traditional machine learning, generative AI, and agentic AI systems.
Architect and deliver scalable, resilient AI solutions leveraging technologies such as agentic systems, large language models (LLMs), distributed systems, and event-driven architectures.
Serve as a senior hands-on contributor, technical leader, and subject matter expert across the AI development lifecycle
Build reusable AI services, frameworks, accelerators, and reference architectures leveraged across multiple teams and use cases
Partner with business domain SMEs, product, engineering, architecture, security, data, and business teams to translate business opportunities into scalable AI solutions
Lead buy, build, and integrate decisions across commercial and open-source AI solutions, balancing speed, cost, risk, security, scalability, and strategic differentiation
Use modern AI-assisted development tooling to improve speed, quality, and efficiency of solution delivery
Guide implementation teams through architecture, design, model selection, evaluation, deployment, and operationalization of AI solutions
Mentor junior AI engineers and elevate the broader organization's AI engineering capabilities
Influence technical direction across teams through expertise, credibility, and collaboration rather than formal authority
Ensure solutions are designed for enterprise requirements including scalability, security, observability, governance, and maintainability
Define enterprise-wide technical direction, including architecture patterns, integration standards, and reusable platform capabilities.
Lead AI-driven modernization of legacy systems through automated code transformation, service decomposition, and continuous optimization.
Design and implement AI-enabled software development lifecycle (SDLC) practices to improve engineering velocity and quality.
Establish technical standards, governance frameworks, and best practices for AI systems, including monitoring, evaluation, and responsible AI.
Partner with engineering teams and stakeholders to enable adoption of AI-assisted development and drive continuous improvement in system performance and productivity.
Provide technical leadership and mentorship while influencing enterprise technology decisions through deep expertise and hands-on engineering.