Senior AI Application Engineer Boston, MA

Join the Managed Services team to build and operate production AI applications that improve how Inter Systems solutions are deployed, patched, upgraded, migrated, and supported across AWS, Google Cloud, Azure, and on-premises environments.

This role is for an engineer who applies agentic AI, LLM-powered tooling, and modern software practices to operational challenges—eliminating manual toil, preventing errors, improving deployment quality, and validating upgrades and migrations. This role requires strong software engineering fundamentals and hands‑on experience operating AI applications in production—not solely prompt engineering, prototypes, or demonstrations. You'll collaborate across teams to turn prototypes into secure, reliable production solutions for healthcare and other regulated environments.

Success in this role is measured by reducing manual operational effort, improving deployment quality, accelerating migrations, and increasing service reliability through the practical application of AI.

• Build AI-powered automation for deployments, patching, upgrades, migrations, and operational workflows across cloud and on-premises environments.
• Build production validation harnesses for deployments and migrations, including automated testing, configuration drift and anomaly detection, and post-deployment checks against real systems.
• Apply LLMs, retrieval-augmented generation (RAG), agentic AI frameworks, and related technologies to create tools that improve operational efficiency and deployment reliability.
• Design and build AI solutions with security, data privacy, reliability, and safety as first-class requirements, reflecting the sensitivity of healthcare and other regulated customer environments.
• Define pre-production evaluation criteria for AI solution accuracy, safety, reliability, and operational effectiveness.
• Design monitoring and observability mechanisms for AI-enabled solutions, including performance, reliability, operational impact, and cost visibility.
• Evaluate and integrate AI-assisted development tools, agent platforms, and automation technologies into CI/CD, testing, and operational workflows.
• Build internal tooling initiatives that demonstrate measurable reductions in manual operational work and can be matured into supportable production solutions.
• Partner with cross-functional teams to integrate AI-driven practices into production workflows and drive adoption.
• Stay current with emerging AI technologies and identify practical opportunities to apply them to Managed Services operations.

• 8+ years of professional software engineering experience building, deploying, and supporting production systems, with strong Python skills and working knowledge of technologies such as Bash, Object Script, Go, Java, or similar languages.
• Demonstrated experience designing, deploying, and supporting production AI applications beyond coursework, hackathons, personal projects, prototypes, or demonstrations.
• Hands‑on experience applying LLMs and AI technologies to real engineering or operational problems, including APIs, agent frameworks, retrieval-augmented generation (RAG), vector databases, guardrails, memory systems, evaluation frameworks, and model monitoring.
• Experience delivering measurable business or operational outcomes through AI, such as reducing manual effort, improving accuracy, accelerating delivery, or increasing service reliability.
• Experience building reliable production validation, testing, automation, or operational frameworks with error handling, reporting, and maintainable integration with real systems.
• Experience supporting production deployments, upgrades, migrations, patching, monitoring, incident response, reliability, or cloud operations across AWS, Google Cloud, Azure, and on-premises environments. Docker, Kubernetes, CI/CD, and Infrastructure-as-Code experience with Terraform, Cloud Formation, Pulumi, or similar tools is required.
• Strong understanding of security, data privacy, and software engineering best practices, with the judgment to apply them when building AI-enabled solutions for sensitive and regulated environments such as healthcare.
• Strong communication and problem-solving skills, with the ability to translate operational challenges into scalable automation. Candidates should be prepared to discuss their contribution to a production AI application, including its architecture, evaluation, security controls, support model, and measurable outcomes.