Deliver internal copilots and customer/agent-facing automations with clear SLAs, rollbacks, and observability from day one.
Design ingestion, chunking, embeddings, indexing, hybrid search/rerank, and retrieval evaluation; track retriever quality via offline golden sets and online metrics.
Design and implement scalable AWS architectures, including AWS AI features such as Bedrock, IAM, knowledge bases, secure secrets and policy enforcement, automated provisioning, and resource-usage governance as core platform capabilities.
Add tracing, prompt/agent version lineage, eval dashboards, and regression alerts; establish golden datasets and canary tests.
Enforce PII redaction, safety filters, role-based access, audit logs, and human-in-the-loop review paths to control quality and risk.
Version and deploy prompts, tools, agents, and retrieval pipelines; support blue/green and shadow deploys with automatic rollback triggers.
Cut run-rate spend through caching, truncation, batching, autoscaling, and model routing; establish clear unit economics per workflow.
Provide templates, SDKs, and high-quality abstractions that let product teams ship safely without bespoke plumbing; improve developer experience.
Build primarily in Python and Metaflow (Outerbounds); deploy on AWS (Bedrock + core services) and OpenAI; use Cursor in daily workflows; help evaluate and, when appropriate, run on Databricks.
Participate in on-call, author runbooks, and remove single-thread risk for AI services; drive reliability and resilience akin to ML Ops.

Requirements

5–10 years of professional software engineering (or equivalent) with 2+ years building AI/LLM applications; portfolio of shipped AI projects (links to code, demos, or case studies).
Demonstrated passion for relentless exploration of the latest AI models, frameworks, and tooling, ensuring constant adoption of state-of-the-art innovations in the workflow.
Hands-on with some/all of OpenAI, Bedrock, Huggingface/Ollama/vLLM; MCP servers and function/tool calling, multi-turn orchestration, streaming, and prompt/version management.
Practical experience designing and tuning retrieval systems (chunking, embeddings, hybrid search, reranking), integration with vector database, and measuring retrieval quality.
Comfortable building APIs/services and simple UIs where needed; strong fundamentals in Python and modern packaging/testing.
CI/CD, containers, cloud fundamentals (AWS), and runtime performance tuning; experience operating services in production.
Metaflow (Outerbounds) preferred; Databricks familiarity is a plus; ability to integrate data/feature pipelines and schedule/operate flows.
Tracing and logging, expertise in tools like Datadog, Dynatrace or Grafana where relevant for AI monitoring is essential.
Comfortable optimizing latency/throughput/cost, and implementing guardrails for PII/safety/compliance.
Partner effectively with data scientists, analysts, and engineers; promote best practices and high-leverage abstractions.
Fine-tuning or distillation experience; Kubernetes or FastAPI exposure; familiarity with Snowflake or similar warehousing for retrieval sources.

Benefits

Pre-tax and post-tax retirement savings plans with a competitive company matching program
Generous paid time-off plans including vacation, personal/sick time, paid short-term and long-term disability leaves, paid parental leave, and paid company holidays
Multiple health care plans to choose from, including dental and vision options
Flexible Spending Plans for Health Care, Dependent Care, and Health Reimbursement Accounts
Company-paid benefits such as life insurance, wellness platforms, employee assistance programs, and Health Advocate programs
Other great discounted benefits include identity theft protection, pet insurance, fitness center reimbursements, and many more!

Applicant Tracking System Keywords

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

Hard Skills & Tools

PythonMetaflowAWSOpenAIHuggingfaceretrieval systemsCI/CDcontainersKubernetesFastAPI

Soft Skills

collaborationproblem-solvingcommunicationreliabilityresilienceexplorationbest practicesdeveloper experiencepartneringoptimizing