Distinguished Engineer

Elevance Health•Indianapolis, IN

1d•Hybrid

About The Position

The Distinguished Engineer leads the design, development, and deployment of large-scale, production-grade artificial intelligence systems and platforms that enable enterprise-wide innovation and modernization. The position sets technical direction and architecture standards, advances AI-driven modernization of legacy systems, and develops AI-enabled software development lifecycle practices. It also establishes governance and responsible-AI practices and partners with engineering teams and stakeholders to support adoption, improve performance and productivity, and influence enterprise technology decisions through hands-on technical leadership and mentorship. The role is hybrid and requires associates to work in-office three days per week. Candidates must live within reasonable commuting distance of a posting location unless an accommodation is granted as required by law. Current and future visa sponsorship is not available.

Requirements

A bachelor's degree in information technology, computer science, engineering, or a related field, and at least 15 years of software engineering experience; an equivalent combination of education and experience may be considered.
Experience leading, designing, implementing, and operating distributed systems and large-scale architectures.
Experience designing, developing, and maintaining software solutions using Python, Java, or comparable object-oriented programming languages.
Experience designing and implementing APIs, microservices, and event-driven architectures.
Experience applying cloud, CI/CD, and DevOps practices.

Nice To Haves

Proven ability to personally design, build, and deliver production-grade solutions while influencing enterprise technology strategy, developing technical talent, and driving measurable business outcomes.
Deep understanding of AI, generative AI, agentic systems, LLMs, intelligent automation, and modern data architectures.
Experience designing and operating large-scale agentic or autonomous systems in production.
Expertise with agent frameworks such as LangChain, Semantic Kernel, or AutoGen; vector databases and retrieval-augmented generation; and LLMOps, evaluation, and monitoring frameworks.
Experience with multi-agent coordination, planning, and memory architecture.
Expertise in cloud-native enterprise data platforms, lakehouse and analytical architectures, governed data products, data integration, interoperability, metadata management, data quality, lineage, and secure access across complex domains.
Hands-on experience designing semantic and context layers, business vocabularies and metrics, ontologies, knowledge graphs, entity relationships and resolution, and reusable knowledge representations connecting enterprise data to AI.
Familiarity with RDF/OWL, SPARQL, SHACL, graph databases, and semantic modeling.
Experience implementing trustworthy natural-language analytics, text-to-SQL with governed semantic definitions, RAG/GraphRAG, semantic retrieval, context engineering, and AI agents that query, reason over, and act on enterprise data.
Attention to provenance, authorization, evaluation, explainability, and numerical accuracy in AI applications.
Ability to apply enterprise data, semantics, analytics, ML, and AI to provider data and analytics, utilization management, prior authorization, clinical interoperability (FHIR/HL7), medical cost management, shift-left payment integrity, value-based care, employer analytics, claims, and care management.
Experience connecting clinical, member, provider, benefit, contract, and policy data.

Responsibilities

Architect and deliver scalable, resilient AI solutions using technologies such as agentic systems, large language models, distributed systems, and event-driven architectures.
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 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 and influence enterprise technology decisions through deep expertise and hands-on engineering.