This role is open to candidates based in LATAM, Africa, and Eastern Europe. Please note that as this role supports U.S.-based clients, candidates must be available to work during U.S. business hours aligned with the client’s time zone.

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.

Role Overview

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.

Location

Fully remote | 9:00 AM - 5:00 PM EST

Key Responsibilities

AI Agent Development & System Architecture

• 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.

Retrieval-Augmented Generation (RAG) & Knowledge Systems

• Build and maintain Retrieval-Augmented Generation (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.

Automation & Business System Integrations

• 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.

Human Oversight, Security & Compliance

• 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.

Cloud Infrastructure & Production Operations

• 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.

Qualifications

Experience

• 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.

• Experience working within regulated industries such as healthcare, finance, or insurance is necessary.

Skills

• Has strong Python programming experience and can use Python to develop production-ready AI applications and supporting systems.

• Experience with Azure AI is a must.

• Has hands-on experience with AI agent and workflow orchestration frameworks such as LangGraph, LangChain, or similar technologies.

• Understands event-driven architectures and can apply them when designing scalable AI workflows and integrations.

• Has strong experience working with LLM APIs, prompt engineering, tool calling, structured outputs, and AI evaluation methods.

• Can build and consume REST APIs, webhooks, and third-party integrations to connect AI applications with existing business systems.

• Has hands-on experience implementing Retrieval-Augmented Generation (RAG) systems using vector databases, embeddings, semantic retrieval, and retrieval quality evaluation.

• Can monitor, troubleshoot, and debug AI applications operating in production environments.

• Has experience deploying applications using AWS, GCP, or similar cloud infrastructure.

• Understands CI/CD pipelines, containerization, secure data handling, and production monitoring practices.

• Can design AI systems with appropriate human-in-the-loop controls, logging, auditability, security, and governance.

• Can communicate complex technical concepts clearly to leadership and other non-technical stakeholders.

Preferred Experience

• 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.

Mindset & Attributes

• Strong ownership mentality with the ability to operate as the technical owner of a system.

• Strategic thinker who understands how technology can solve practical business problems.

• Strong judgment around AI safety, governance, security, and reliability.

• Pragmatic builder focused on delivering working solutions and continuously improving them.

• Comfortable communicating technical concepts to non-technical stakeholders.

• Self-directed and capable of working independently.

• Strong professional English communication skills.

• Excited about building practical AI systems that create measurable business impact.

What Success Looks Like

• A reliable production AI system is successfully designed, deployed, and maintained.

• AI agents automate meaningful research, content, and business development workflows.

• RAG systems provide accurate and reliable knowledge retrieval.

• AI workflows incorporate appropriate human approval and governance controls.

• Existing business systems are successfully integrated with AI capabilities.

• AI applications remain monitored, secure, reliable, and scalable.

• Leadership has confidence that AI systems operate reliably and meet applicable compliance requirements.

• Responsible AI adoption creates meaningful improvements in organizational efficiency.

Opportunity

This is an opportunity to become the technical owner of an organization's AI transformation. Rather than building isolated AI features, you will design and operate an AI ecosystem that directly impacts how the organization researches, creates, and grows.

You will work closely with leadership, contribute to the organization's AI direction, and build systems that amplify human expertise. For an engineer passionate about agentic AI, LLM applications, automation, and building production systems from the ground up, this role offers the opportunity to make a significant long-term impact.

Application Process:

To be considered for this role these steps need to be followed:

• Fill in the application form

• Record a video showcasing your skill sets