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United States
Remote only
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.
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.
The ideal candidate is an experienced software engineer with deep practical knowledge of production AI systems, strong Python and backend development skills, and the ability to lead technical initiatives while remaining hands-on.
Required qualifications
U.S. citizenship or authorization to work in the United States.
At least 7 years of professional software engineering experience, including substantial responsibility for designing and delivering production systems.
Strong proficiency in Python and modern backend software engineering.
Demonstrated experience providing technical leadership for engineering projects or small teams, including architecture, implementation, mentorship, and delivery.
Proven hands-on experience designing, building, and deploying production AI applications using LLMs, agentic workflows, retrieval systems, or related technologies.
Strong understanding of backend architecture, APIs, distributed systems, databases, and workflow orchestration.
Experience designing agentic workflows, tool-calling systems, structured outputs, or retrieval-augmented generation architectures.
Knowledge of production AI challenges, including model reliability, hallucination mitigation, evaluation, observability, latency, and cost management.
Commitment to software engineering best practices, including automated testing, code reviews, CI/CD, system design, and maintainability.
Experience working with cloud infrastructure, preferably AWS.
Ability to mentor engineers, provide constructive technical feedback, and influence engineering practices.
Strong technical judgment, debugging capabilities, and the ability to navigate complex systems and ambiguous requirements.
Ability to balance hands-on engineering contributions with technical leadership responsibilities.
Excellent communication and collaboration skills, particularly when working across engineering, product, and business teams.
Adaptability and strong ownership in fast-moving environments where execution and accountability are essential.
Preferred qualifications
Experience with Amazon Bedrock, AWS AgentCore, Lambda, Step Functions, S3, DynamoDB, RDS, SQS, or related AWS services.
Familiarity with LangGraph, LangChain, DSPy, Semantic Kernel, or similar agent orchestration frameworks.
Experience building multi-step autonomous agents that interact with tools, APIs, external systems, or business workflows.
Experience developing AI evaluation frameworks, prompt regression testing, trace analysis, or human-in-the-loop review systems.
Familiarity with vector databases and semantic retrieval technologies such as OpenSearch, pgvector, Pinecone, Weaviate, or FAISS.
Experience with LLM observability and evaluation platforms such as LangSmith, Arize, Helicone, Weights & Biases, or OpenTelemetry.
Experience leading technical initiatives in startup environments or rapidly scaling SaaS organizations.
Track record of improving engineering practices, introducing development standards, or strengthening technical maturity across teams.
Ability to balance technical debt, delivery speed, operational reliability, and long-term architectural decisions.
Personal qualities and success indicators
Takes ownership and leads through technical expertise, sound judgment, and example.
Can break down complex technical challenges, establish clear solutions, and guide teams through successful implementation.
Values engineering craftsmanship while maintaining a pragmatic focus on delivering business value.
Enjoys mentoring colleagues, challenging assumptions, and helping others become more capable and independent.
Takes responsibility for both individual contributions and the quality of the work delivered by the teams they support.
Demonstrates the ability to deliver high-impact initiatives, build reliable and observable AI systems, improve engineering quality, and strengthen the technical capabilities of colleagues.
Fully remote work environment.
Opportunity to work on advanced AI applications, autonomous agents, and production-scale intelligent systems.
Significant technical ownership and influence over architecture, engineering practices, and product capabilities.
Opportunity to lead and mentor engineers while remaining hands-on with implementation.
Collaborative environment focused on innovation, engineering excellence, and meaningful business outcomes.
Opportunity to help develop AI solutions supporting nonprofit fundraising and organizations with important social missions.