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Distinguished Engineer
Elevance Health. Lead the design, development, and deployment of large-scale, production-grade AI systems and platforms .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying large-scale AI systems, with a strong focus on distributed architectures, cloud-native solutions, and AI-driven modernization. Proven ability to lead technical direction, establish governance frameworks, and mentor engineering teams to enhance productivity and system performance.
Highest-signal resume keywords
Large-Scale AI Systems DesignDistributed Systems ArchitecturePython ProgrammingCloud, CI/CD, and DevOps PracticesAI and Generative AI Expertise
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Software EngineeringAPI Design and ImplementationMicroservices ArchitectureEvent-Driven ArchitecturesAgent FrameworksVector DatabasesLLMOpsNatural-Language AnalyticsSemantic ModelingData Integration
Soft Skills
Technical LeadershipMentorshipCollaborationInfluencing Technology DecisionsAttention to Detail
Tools & Technologies
LangChainSemantic KernelAutoGenGraph DatabasesRDF/OWLSPARQLSHACLKnowledge GraphsContext EngineeringAI Agents
Industry Keywords
Healthcare DataFHIR/HL7Claims ManagementUtilization ManagementValue-Based CareDecision IntelligenceData QualityProvenanceExplainabilityNumerical Accuracy
Tech Stack
Tools & technologiesCloudDistributed SystemsJavaMicroservicesPythonSQL
About the role
Key responsibilities & impact- Lead the design, development, and deployment of large-scale, production-grade AI systems and platforms
- Architect and deliver scalable, resilient AI solutions using agentic systems, LLMs, distributed systems, and event-driven architectures
- Define enterprise-wide technical direction, 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 drive adoption of AI-assisted development and improve system performance and productivity
- Provide technical leadership and mentorship while influencing enterprise technology decisions through deep expertise and hands-on engineering
- Personally design, build, and deliver production-grade solutions while influencing enterprise technology strategy and driving measurable business outcomes
Requirements
What you’ll need- Bachelor’s degree in information technology, Computer Science, Engineering, or a related field, and a minimum of 15 years of experience in software engineering, or an equivalent combination of education and experience
- 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
- 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
- Experience with agent frameworks such as LangChain, Semantic Kernel, and AutoGen
- Experience with vector databases and retrieval-augmented generation (RAG)
- Experience with LLMOps, evaluation, and monitoring frameworks
- Experience with multi-agent coordination, planning, and memory architecture
- Deep 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
- Hands-on experience designing semantic and context layers, business vocabularies and metrics, ontologies, knowledge graphs, entity relationships and resolution, and reusable knowledge representations
- Familiarity with RDF/OWL, SPARQL, SHACL, graph databases, and semantic modeling
- Experience implementing natural-language analytics, text-to-SQL, RAG/GraphRAG, semantic retrieval, context engineering, and AI agents operating on enterprise data
- Demonstrated attention to provenance, authorization, evaluation, explainability, and numerical accuracy
- Ability to apply enterprise data, semantics, analytics, ML, and AI to healthcare data and decision intelligence
- Experience with FHIR/HL7, claims, care management, utilization management, prior authorization, medical cost management, payment integrity, value-based care, employer analytics, and related healthcare data
Benefits
Comp & perks- Merit increases
- Paid holidays
- Paid Time Off
- Incentive bonus programs
- Medical benefits
- Dental benefits
- Vision benefits
- Short-term disability benefits
- Long-term disability benefits
- 401(k) with match
- Stock purchase plan
- Life insurance
- Wellness programs
- Financial education resources
- Hybrid work arrangement
- Flexible work supporting productivity and work-life balance
- Professional and personal growth opportunities