Senior Applied AI Engineer

What You’ll Work On

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) systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly
• Integrate foundation models through platforms such as Amazon Bedrock or Agent Core
• Develop robust prompting strategies, structured outputs, guardrails, and workflow logic for production use cases
• Implement evaluation systems for prompts, agents, and workflows, including regression testing, trace review, golden datasets, and human QA processes
• Monitor and improve production AI systems for quality, reliability, latency, observability, and cost efficiency
• Debug AI behavior through logs, traces, evaluations, user feedback, and production telemetry
• Collaborate closely with engineering, product, operations, and customer-facing teams to turn ambiguous requirements into reliable systems
• Help establish strong engineering standards around testing, deployment, CI/CD, version control workflows, code review, and operational reliability
• Mentor and collaborate with engineers across both software and AI disciplines
• Evaluate emerging AI technologies pragmatically based on business impact, maintainability, and operational reliability

Required Qualifications

• US Citizen or authorized to work in US
• 5+ years of professional software engineering experience building production systems
• Strong proficiency in Python
• Strong backend engineering fundamentals and experience building scalable APIs, services, distributed systems, or workflow orchestration platforms
• Proven hands-on experience building and shipping AI-powered applications using LLMs, generative AI APIs, agents, retrieval systems, or related technologies in production environments
• Experience designing and implementing agentic workflows, tool-calling systems, structured outputs, prompt pipelines, or retrieval-augmented generation architectures
• Strong understanding of the practical challenges involved in production AI systems, including hallucination mitigation, evaluation, reliability, observability, latency, and cost management
• Experience building production software systems with strong engineering standards around testing, QA, deployment, monitoring, and maintainability
• Strong understanding of modern software engineering practices, including Git workflows, code review, CI/CD, automated testing, operational debugging, and release management
• Experience working with cloud infrastructure, preferably AWS
• Experience working 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 and imperfect information
• Strong communication skills and ability to collaborate across technical and non-technical teams

Preferred Qualifications

• Experience with Amazon Bedrock, AWS Lambda, Step Functions, S3, DynamoDB, RDS, SQS, EventBridge, or related AWS services
• Experience with LangGraph, LangChain, DSPy, Semantic Kernel, or similar orchestration frameworks
• Experience building multi-step agents that interact with tools, APIs, external systems, or business workflows
• Experience implementing AI evaluation systems, prompt regression testing, trace analysis, or human-in-the-loop review workflows
• Experience with vector databases and semantic retrieval systems such as OpenSearch, pgvector, Pinecone, Weaviate, FAISS, or similar technologies
• Experience with observability and LLMOps tooling such as LangSmith, Arize, Helicone, Weights & Biases, OpenTelemetry, or similar platforms
• Experience balancing quality, latency, reliability, and cost tradeoffs in production AI systems
• Experience mentoring engineers and helping establish strong engineering culture and development practices
• Experience working in startup or high-ownership product environments
• Ability to think critically about edge cases, failure modes, operational risk, and long-term maintainability

What Success Looks Like