Generative AI Applications Engineer (Agents & RAG)
Role details
Job location
Tech stack
Job description
You'll turn mission needs into secure, reliable, and scalable GenAI applications no model training required. This is a hands-on role across agentic workflows, RAG, prompt/policy design, LLM evaluation, and platform integration. You'll own the end-to-end path from use case evaluation production deployment operational excellence, partnering with product, security, data, and SRE to ship features safely and at scale.
What You'll Do (Day to Day)
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Design & ship mission grade GenAI: Build agentic workflows and RAG systems tailored to mission data and environments; target low hallucination, tight p95 latency, and predictable cost.
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Agent frameworks & orchestration: Apply patterns from LangChain/LlamaIndex/Semantic Kernel; design task decomposition, tool use, guardrails, and recovery/fallback strategies.
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Platform integration (no model training): Implement with AWS Bedrock, Azure OpenAI, Google Vertex AI, Amazon Kendra, and managed services (e.g., Document AI, Gemini, Gemma).
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LLM selection & evaluation: Compare models for quality, safety, latency, cost; author/test prompts & policies; deploy with observability and safe rollback/fallback.
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RAG done right: Build retrieval pipelines & vector search (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma); handle data prep, chunking, metadata, and IRstyle evals (e.g., NDCG) to maximize signal to noise.
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Production rigor: Instrument metrics/logs/traces; run A/B experiments; maintain incident playbooks; and implement safety & compliance guardrails.
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SRE & FinOps for AI: Define SLIs/SLOs (quality/latency/safety/cost), run on call and postmortems, reduce MTTR; meter usage and optimize token/spend.
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Reusable platform components: Ship SDKs, CI/CD templates, Terraform/IaC modules, evaluation harnesses that accelerate multiple mission team not one-off projects.
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Operate in real world constraints: Deliver into hybrid, restricted, or air gapped environments with Zero Trust principles and audit ready controls.
You'll Thrive Here If you have
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End-to-end ownership of production systems: integration deployment observability incident response.
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Hands-on experience with LLMs, transformer based apps, and RAG in production.
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Strong Python
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Experience with vector search and retrieval (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma) and grounding AI in enterprise/mission data.
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U.S. Citizenship
Nice to Have
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Integration with leading cloud AI services or on prem inference stacks
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Background in LLM evaluation, prompt authoring/testing, A/B experimentation, and LLM Ops.
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Responsible AI expertise (privacy, security, bias, transparency, human in the loop) and data governance.
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Experience implementing tool using agents for API integration and external data access.
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Containerization & orchestration (Docker, Kubernetes, VMware) and scripting/automation (Linux Bash, PowerShell).
Requirements
Do you have experience in Zero Trust security?
Benefits & conditions
3.73.7 out of 5 stars San Francisco, CA Remote $103,200 - $203,400 a year