Applied AI Engineer

About the role

We’re hiring an Applied AI Engineer to build production AI systems that real customers depend on. This role is for an experienced software engineer who also understands modern AI systems, comfortable with LLMs, agents, retrieval pipelines, and workflow orchestration.

Key responsibilities

• Design, build, and maintain production‑grade AI systems and customer‑facing AI features.
• Develop agentic workflows using LLMs, retrieval systems, tools, APIs, and backend services.
• Build backend services, orchestration systems, automation, and infrastructure supporting AI‑powered workflows.
• Design and implement retrieval‑augmented generation (RAG) pipelines, including ingestion, embeddings, and semantic retrieval.
• Integrate foundation models via platforms such as Amazon Bedrock or Agent Core.
• Develop robust prompting strategies, guardrails, and workflow logic for production use cases.
• Implement evaluation systems for prompts, agents, and workflows, including regression testing and human QA.
• Monitor and improve production AI systems for quality, reliability, latency, observability, and cost efficiency.
• Debug AI behavior using logs, traces, evaluations, user feedback, and telemetry.
• Collaborate with engineering, product, operations, and customer‑facing teams to turn ambiguous requirements into reliable systems.

Required profile

• US citizen or authorized to work in the US.
• 5+ years of professional software engineering experience building production systems.
• Strong proficiency in Python and backend engineering fundamentals.
• Hands‑on experience building and shipping AI‑powered applications using LLMs, generative AI APIs, agents, or retrieval systems.
• Deep understanding of production AI challenges such as hallucination mitigation, evaluation, reliability, observability, latency, and cost management.
• Experience with cloud infrastructure, preferably AWS, and with SQL and/or NoSQL databases.
• Strong debugging, systems‑thinking, and problem‑solving skills.
• Ability to operate effectively in fast‑moving environments with evolving requirements.

Required skills

• Python
• AWS (Amazon Bedrock, Lambda, Step Functions, S3, DynamoDB, RDS, SQS, EventBridge)
• LangChain / LangGraph / DSPy / Semantic Kernel
• Vector databases and semantic retrieval (OpenSearch, pgvector, Pinecone, Weaviate, FAISS)
• Observability and LLMOps tools (LangSmith, Arize, Helicone, Weights & Biases, OpenTelemetry)
• Git, CI/CD, automated testing, and release management
• SQL and NoSQL databases