Partner with stakeholders to identify, evaluate, document, and shape GenAI use cases (copilots, automation, decision support, and insight generation) with clear success metrics.
Design solution architectures that integrate LLMs with enterprise systems, data sources, and tool/function calling while meeting latency and reliability expectations.
Develop prototypes rapidly and validate them through evaluation, red-teaming, and user feedback; document tradeoffs and recommendations.
Build production-grade services and full-stack experiences (APIs, UIs, workflows) with secure authentication/authorization, audit logging, and scalable deployment patterns.
Implement safety, privacy, and compliance controls (e.g., PHI/PII protection, prompt injection defenses, data residency constraints, and policy-based filtering).
Instrument solutions end-to-end with metrics, traces, logs, and model/app observability; contribute to SLOs, error budgets, and operational runbooks.
Build and maintain evaluation harnesses for LLM quality, safety, and business outcomes (offline tests, golden sets, regression suites, and online experiments).
Implement RAG pipelines (chunking, embedding, vector search, reranking) and optimize for accuracy, cost, and latency.
Collaborate with platform teams on deployment, monitoring, drift/quality detection, and incident response for model-backed services.
Contribute reusable libraries and patterns for prompt management, retrieval, tool calling, and policy enforcement.
Participate in design reviews and code reviews; mentor senior and mid-level engineers on GenAI engineering practices.
Continuously improve developer experience through templates, CI/CD automation, and documentation that accelerates safe adoption.

Requirements

7+ years of software engineering supporting Data or AI/ML initiatives, including building and operating production services.
3+ years applying ML/AI in production; demonstrated hands-on GenAI delivery (LLMs, RAG, evaluation, and safety controls)
3+ years of experience delivering solutions in high-scale, high-availability environments with strong security and compliance requirements.
Strong full-stack engineering skills (backend services, APIs, and modern web application development) with a focus on reliability and security.
Hands-on expertise with LLM application patterns: RAG, tool/function calling, prompt management, evaluation, and guardrails.
Experience with Python and at least one additional backend language; familiarity with common ML libraries and serving frameworks.
Working knowledge of containerization and Kubernetes, CI/CD, infrastructure-as-code concepts, and production observability.
Ability to communicate clearly, influence across teams, and translate business needs into implementable technical plans.

Benefits

Affordable medical plan options
401(k) plan (including matching company contributions)
Employee stock purchase plan
No-cost programs for all colleagues including wellness screenings, tobacco cessation and weight management programs, confidential counseling and financial coaching.
Benefit solutions that address the different needs and preferences of our colleagues including paid time off, flexible work schedules, family leave, dependent care resources, colleague assistance programs, tuition assistance, retiree medical access and many other benefits depending on eligibility.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

GenAILLMsRAGPythonbackend servicesAPIsmodern web application developmentcontainerizationKubernetesCI/CD

Soft Skills

communicationinfluencecollaborationmentoringdocumentationdeveloper experience improvementevaluationuser feedbackdesign reviewscode reviews