Senior AI Application Engineer

Responsibilities

• Build AI-powered automation for deployments, patching, upgrades, migrations, and operational workflows across cloud and on-premises environments.
• Develop production validation harnesses with automated testing, configuration drift and anomaly detection, reporting, and post-deployment checks against real systems.
• Apply LLMs, retrieval-augmented generation, agentic AI frameworks, and related technologies to improve operational efficiency and deployment reliability.
• Design AI solutions with security, data privacy, reliability, and safety requirements for healthcare and other regulated environments.
• Define pre-production evaluation criteria for AI accuracy, safety, reliability, and operational effectiveness.
• Design monitoring and observability 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 that reduces manual operational work and mature prototypes into supportable production solutions.
• Partner with cross-functional teams to integrate AI-driven practices into production workflows and drive adoption.
• Track emerging AI technologies and identify practical applications for Managed Services operations.

Requirements

• 8+ years of professional software engineering experience building, deploying, and supporting production systems.
• Strong Python skills and working knowledge of Bash, ObjectScript, 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 with LLM APIs, agent frameworks, retrieval-augmented generation, vector databases, guardrails, memory systems, evaluation frameworks, and model monitoring.
• Experience delivering measurable operational or business outcomes through AI.
• Experience building reliable production validation, testing, automation, or operational frameworks that integrate with real systems.
• Experience with production deployments, upgrades, migrations, patching, monitoring, incident response, reliability, or cloud operations across AWS, Google Cloud, Azure, and on-premises environments.
• Required experience with Docker, Kubernetes, CI/CD, and Infrastructure-as-Code using Terraform, CloudFormation, Pulumi, or similar tools.
• Strong understanding of security, data privacy, and software engineering practices for sensitive and regulated environments.
• Strong communication and problem-solving skills, including the ability to explain production AI architecture, evaluation, security controls, support models, and measurable outcomes.
• Preferred background in cloud migrations, patch management, release automation, or platform modernization.
• Preferred experience integrating commercial or open-source foundation models into production AI applications.
• Open-source contributions in AI, cloud, automation, or software engineering are preferred.
• Experience in regulated industries such as healthcare, life sciences, financial services, or government is preferred.
• Familiarity with AI-assisted development tools such as GitHub Copilot, Claude Code, Cursor, Tabnine, Replit, or equivalents is preferred.

Benefits

• Comprehensive medical, dental, and vision insurance.
• Disability and life insurance.
• 401(k) profit-sharing contribution.
• Generous paid time off and paid holidays.
• Paid parental leave.
• Tuition reimbursement for continued learning and professional development.
• Company-sponsored outings and employee events.
• Cafeteria stipend.
• Convenient onsite gym.