• Analyze, design, program, debug, test, implement, deploy, and support software enhancements and new applications using Generative AI technologies • Contribute to the development and production deployment of GenAI-enabled applications, including LLM-powered workflows, RAG pipelines, and AI-driven user experiences • Support SDLC documentation across all phases, with a focus on deployment, evaluation, observability, safety, and monitoring • Interact with users to define requirements and support applications in production • Develop and modify application modules, including GenAI components • Build prompt workflows, retrieval layers, APIs, and cloud services • Troubleshoot production issues, including latency, hallucinations, and errors • Provide Level II production support for deployed systems • Design components, including LLM integrations and RAG pipelines • Implement CI/CD pipelines, containerization, and release processes • Develop RAG pipelines with embeddings, chunking, and vector search • Apply prompt engineering techniques, including few-shot prompting and structured outputs • Evaluate models for accuracy, relevance, and hallucination risk • Implement safety guardrails, including PII protection and prompt-injection defense • Execute testing, including unit, integration, and GenAI evaluation testing • Monitor production systems for latency, cost, usage, and errors • Support incident management with fallback and recovery strategies

• Manage multiple AWS accounts with tools like AWS Control Tower and Terraform • You will deploy and debug cloud stacks, educating teams on new cloud projects, and ensuring the security of the cloud infrastructure • As a SRE, you can identify the most optimal cloud-based solutions for our internal users, and maintain cloud infrastructures following industry leading practices and company security policies • This is a 24/7 on-call position with a rotation schedule

• Drive the reliability and observability initiative • Own the reliability roadmap end to end. • Prove a repeatable define → emit → ingest → dashboard → alert metric pipeline, set SLOs and error budgets, prioritize the work, and drive execution. • You'll partner with engineering on what we monitor, how, and when — indexing on user impact over low-level infrastructure. • Harden the foundational platform • Take the financial data platform from functional to enterprise-grade, with a focus on availability, performance, and recoverability. • Strengthen deployment paths, straight-through processing, and failover so the monthly close runs faster and cleaner as legacy hops are retired. • Expand observability breadth and depth • Extend instrumentation across the six target systems — Velocity, Red Panda, MuleSoft, Snowflake, Fabric, and AWS (with D365 ledger to follow) — proving both push (OpenTelemetry) and pull (agent) ingestion. • Cover service health (latency, error rates, throughput) and business KPIs (match rate, reconciliation completeness, settlement correctness and latency). • Implement a scalable incident and review process • Build the on-call, alerting, and blameless postmortem process that keeps reliability high as systems and the team grow. • Route alerts Datadog → Incident.io with ServiceNow as the system of record, and set severity standards, escalation norms, and follow-up tracking that actually closes the loop. • Scale automation, auditability, and reduce toil • Build the tooling that automates routine operations, self-heals common failures, and surfaces signal over noise. • Establish data lineage and retention, and validate reliability at scale — 5,000+ transactions before go-live — through auto-remediation, capacity planning, and actionable dashboards. • Build specialized SRE agents using Cursor AI • Design and ship AI agents for incident triage, log analysis, and root-cause investigation (to name a few). • Use Cursor as your build environment. • Treat the agents as products solving specific problems. • Host SRE agents on the AI fabric • Partner with the AI platform team to deploy your agents on the org's AI fabric. Make them discoverable, governed, and reusable across functions.

• Lead the standardization of application deployments, containerization, and release processes • Design and implement consistent, repeatable deployment pipelines • Containerize applications using Docker and orchestration platforms like ECS or Kubernetes • Establish shared CI/CD frameworks • Create tooling and documentation that empowers engineering teams to ship reliably • Define best practices and golden paths for deployment