Distinguished AI Engineer

Elevance Health•Indianapolis, IN

1d•Hybrid

About The Position

The Distinguished AI Engineer is a highly experienced, hands-on individual contributor who designs, builds, and scales enterprise AI solutions across machine learning, generative AI, and AI agents. The role combines deep technical expertise and business acumen to develop production-grade AI capabilities and influence architecture, delivery models, and engineering practices across the organization. It is a hands-on engineering role, not a pure research or management position, and includes making buy, build, and integration decisions across commercial and open-source solutions based on business needs, regulatory requirements, speed to value, and maintainability. The engineer provides technical leadership throughout the AI development lifecycle, building reusable services and guiding teams in architecture, design, model selection, evaluation, deployment, and operations. The role partners with business and technology stakeholders, mentors other engineers, and helps establish enterprise-wide patterns, standards, and governance for scalable, secure, observable, and maintainable AI systems. It also leads AI-driven modernization of legacy systems and enables AI-assisted software development practices. The position follows a hybrid workforce strategy and requires associates to work in-office one day per week. The preferred location is the Pulse Point office in Indianapolis, Indiana; alternate locations may be considered for candidates within commuting distance of an office.

Requirements

Bachelor’s degree in Computer Science, Information Technology, or a related field and at least 15 years of experience in software engineering, with deep expertise in distributed systems and large-scale architecture; an equivalent combination of education and experience is acceptable.
Experience delivering production-grade AI/ML systems at scale.
Experience building agentic systems or complex LLM-based applications.
Experience modernizing large, complex legacy systems and delivering high-quality systems.
Strong programming expertise in Python, Java, or a similar language.
Deep experience with APIs, microservices, event-driven architectures, cloud platforms, CI/CD, and DevOps practices.

Nice To Haves

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

Responsibilities

Design, develop, and deploy enterprise AI solutions across traditional machine learning, generative AI, and agentic AI systems.
Architect and deliver scalable, resilient AI solutions using agentic systems, large language models, 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 for use across teams and business applications.
Partner with business domain subject matter experts and product, engineering, architecture, security, data, and business teams to turn opportunities into scalable AI solutions.
Lead buy, build, and integration decisions involving commercial and open-source AI solutions, balancing speed, cost, risk, security, scalability, and strategic differentiation.
Use modern AI-assisted development tools to improve solution delivery speed, quality, and efficiency.
Guide implementation teams through architecture, design, model selection, evaluation, deployment, and operationalization of AI solutions.
Mentor junior AI engineers and improve the organization's AI engineering capabilities.
Influence technical direction across teams through expertise, credibility, and collaboration.
Ensure AI solutions meet enterprise requirements for scalability, security, observability, governance, and maintainability.
Define enterprise 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 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 continuously improve system performance and productivity.
Provide technical leadership and mentorship while influencing enterprise technology decisions through hands-on engineering expertise.