Implement AI-powered services and application features that integrate enterprise data and systems with LLMs and ML models
Contribute to solution design by proposing approaches, identifying dependencies, and documenting tradeoffs and implementation plans
Deliver features using strong engineering practices: test automation, code review, CI/CD, and operational readiness
Build and tune RAG pipelines (retrieval, ranking, grounding) and implement prompt patterns that improve reliability and user experience
Contribute to evaluation suites and regression testing to ensure quality and safety over time
Implement guardrails and security controls (input/output validation, policy filtering, and data protection patterns) under guidance from senior engineers
Instrument services with metrics, traces, logs, and dashboards; participate in incident response and post-incident improvements
Support performance and cost optimization efforts (latency, throughput, caching, and model selection)
Follow and reinforce platform standards for deployment, versioning, monitoring, and documentation
Work closely with product, design, security, and platform teams to refine requirements and deliver customer-focused outcomes
Mentor engineers at earlier career stages and share practical learnings with the broader AI/ML community of practice
Stay current on GenAI and ML platform trends; propose incremental improvements that raise quality and accelerate delivery.

Requirements

5+ years of software engineering experience delivering production systems, including modern APIs and application development
3+ years of experience applying ML/AI concepts in real systems; GenAI exposure (LLMs, RAG, evaluation) strongly preferred
3+ years of experience delivering solutions in high-scale, high-availability environments with strong security and compliance requirements
Proficiency in backend engineering (APIs, services)
Working knowledge of LLM application development (prompting, RAG, tool calling) and evaluation practices
Experience with CI/CD, testing, containerization, and basic Kubernetes concepts
Strong debugging and operational skills; ability to improve reliability and performance based on telemetry and root cause analysis
Effective collaboration and communication skills across technical and non-technical stakeholders.

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
Paid time off
Flexible work schedules
Family leave
Dependent care resources
Colleague assistance programs
Tuition assistance
Retiree medical access

Applicant Tracking System Keywords

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

Hard Skills & Tools

AI-powered servicesML modelstest automationcode reviewCI/CDRAG pipelinesinput/output validationdata protection patternsbackend engineeringdebugging

Soft Skills

collaborationcommunicationmentoringproblem-solvingdocumentation