AI Engineer (Python)

Our client is an organization focused on using artificial intelligence to automate complex knowledge-based workflows and improve operational efficiency. They are building an internal multi-agent AI ecosystem designed to support research, content, business development, and other business processes while maintaining strong standards for security, governance, auditability, and human oversight. As the AI Engineer, you will be responsible for designing, building, deploying, and operating an internal multi-agent AI system that automates complex knowledge-based workflows. You will own the AI system end-to-end, from architecture and development through deployment, integration, monitoring, and continuous improvement. Working directly with leadership, you will translate business requirements into reliable AI solutions while ensuring appropriate governance, security, auditability, and human oversight. This is a build-and-own role for an engineer with experience taking AI applications into production who is comfortable owning complete systems rather than working only on isolated technical tasks.

Responsibilities

Design, build, and maintain production-ready multi-agent AI systems; Develop AI solutions that automate research, content, and business development workflows; Architect scalable agent workflows with appropriate human oversight and governance; Define engineering standards and best practices for AI system development; Own the long-term operation, maintenance, and continuous improvement of AI systems; Build and maintain RAG systems using vector databases; Develop knowledge retrieval workflows using embeddings, semantic search, and retrieval optimization; Evaluate retrieval quality and continuously improve knowledge system performance; Create governed AI knowledge systems designed to provide reliable information; Integrate AI systems with existing business platforms through APIs and webhooks; Connect AI workflows with CRM systems, communication platforms, scheduling tools, and content platforms; Build reliable integrations that allow AI systems to interact with existing business processes; Partner with external developers or technical vendors when needed while serving as the internal technical owner; Build human-in-the-loop approval workflows for external-facing AI actions; Design AI systems with reliability, safety, governance, and auditability as core requirements; Implement logging, monitoring, and audit trails across AI workflows; Ensure AI systems follow applicable compliance requirements within a regulated healthcare environment; Support secure deployment practices, including role-based access controls, encryption, and secure cloud infrastructure; Deploy and maintain AI applications within secure cloud environments; Support cloud infrastructure using AWS, GCP, or similar platforms; Build and maintain CI/CD workflows, containerized deployments, and production monitoring; Establish effective debugging, observability, and reliability practices; Continuously improve AI system performance, stability, and scalability

Requirements

Availability to work during U.S. business hours aligned with the client's time zone; 3-5 years of professional software engineering experience; Recent hands-on experience building and deploying LLM-based or agentic AI systems into production; Experience owning technical systems from architecture and development through deployment and ongoing maintenance; Demonstrated ability to independently design, build, and maintain production applications; Previous experience working with healthcare data is a requirement; Strong Python programming experience and ability to develop production-ready AI applications and supporting systems; Experience with Azure AI is a must; Hands-on experience with AI agent and workflow orchestration frameworks such as LangGraph, LangChain, or similar technologies; Understanding of event-driven architectures; Strong experience with LLM APIs, prompt engineering, tool calling, structured outputs, and AI evaluation methods; Ability to build and consume REST APIs, webhooks, and third-party integrations; Hands-on experience implementing RAG systems using vector databases, embeddings, semantic retrieval, and retrieval quality evaluation; Ability to monitor, troubleshoot, and debug AI applications operating in production environments; Experience deploying applications using AWS, GCP, or similar cloud infrastructure; Understanding of CI/CD pipelines, containerization, secure data handling, and production monitoring practices; Ability to design AI systems with human-in-the-loop controls, logging, auditability, security, and governance; Ability to communicate complex technical concepts clearly to leadership and non-technical stakeholders; Strong professional English communication skills; Self-directed and capable of working independently

Nice to have

Experience building systems aligned with HIPAA, SOC 2, or similar audit and compliance requirements is preferred; Experience integrating CRM and marketing platforms with AI-powered systems is considered a plus; Experience with voice AI or conversational AI systems is considered a plus