Senior Applied AI Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Applied AI Engineer based in the United States. Take a leading role in designing and delivering sophisticated AI-powered applications that solve complex business challenges and create meaningful customer impact. As a senior individual contributor, you will combine hands-on software engineering with technical leadership, guiding small engineering teams through the development of production-grade AI capabilities. You will work across agentic workflows, large language models, retrieval systems, and autonomous business processes to build intelligent, reliable product experiences. Beyond implementation, you will shape architectural decisions, translate ambiguous requirements into actionable plans, and drive initiatives from concept through production. You will also strengthen engineering standards through mentorship, code reviews, and technical guidance. This is an opportunity to influence both the evolution of AI products and the technical maturity of a fast-moving, fully remote organization.

Accountabilities

You will lead high-impact AI engineering initiatives while remaining actively involved in implementation, ensuring that technical solutions are scalable, reliable, maintainable, and aligned with business objectives.

Technical Leadership and Delivery

• Lead small engineering teams in designing, developing, and delivering complex AI initiatives.
• Own technical execution throughout the project lifecycle, from initial architecture and implementation to testing, deployment, and ongoing operations.
• Translate ambiguous product requirements into clear technical strategies, actionable engineering tasks, and realistic delivery milestones.
• Drive architectural discussions, assess technical trade-offs, and establish effective implementation approaches.
• Coordinate engineering activities, manage dependencies, resolve blockers, and maintain accountability for delivery commitments.
• Contribute production code and work alongside engineers to solve challenging technical problems.
• Collaborate with Product, Engineering leadership, and business stakeholders to ensure solutions deliver measurable value.

Applied AI Engineering

• Design, build, and maintain production-grade AI applications using LLMs, agentic workflows, retrieval architectures, and backend services.
• Develop advanced agent workflows incorporating tool calling, structured outputs, multi-step reasoning, deterministic validation, and human-in-the-loop controls.
• Build and optimize retrieval-augmented generation (RAG) systems, including ingestion pipelines, embeddings, semantic search, and context assembly.
• Integrate foundation models through platforms such as Amazon Bedrock and AWS AgentCore.
• Develop AI evaluation frameworks using regression testing, golden datasets, trace analysis, and quality measurement.
• Improve production AI systems across reliability, accuracy, latency, observability, and cost efficiency.
• Design robust systems capable of handling unpredictable model behavior, edge cases, operational failures, and real-world production constraints.
• Build and improve orchestration systems, infrastructure, backend services, APIs, and integrations that enable AI systems to interact safely with production applications.

Engineering Excellence and Mentorship

• Raise engineering standards through code reviews, architectural guidance, hands-on mentorship, and technical collaboration.
• Mentor engineers in software design, debugging, testing strategies, system architecture, and production engineering practices.
• Establish best practices for code quality, automated testing, documentation, maintainability, and operational readiness.
• Help engineers strengthen their technical judgment, ownership, independence, and problem-solving capabilities.
• Identify recurring technical weaknesses, unnecessary complexity, and process inefficiencies, and implement improvements.
• Promote engineering practices that prioritize reliability, clarity, testability, and long-term maintainability.
• Lead by example through high-quality implementation, constructive feedback, effective collaboration, and personal accountability.
• Improve development tooling and workflows to increase engineering productivity, software quality, and operational reliability.

Requirements The ideal candidate is an experience…