Lead AI Solution Engineer - LMTS

Lead Ai Solution Engineer – LMTS

Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.

athenahealth is a progressive, innovation-driven software product company. We partner with healthcare organizations across the care continuum to drive clinical and financial results. Our expert teams build modern technology on an open, connected ecosystem, yielding insights that make a difference for our customers and their patients. We maintain a unique values-driven employee culture and offer a flexible work-life balance. As evidence of our rapid growth and industry leadership, we were acquired by the world's leading private equity firm Bain Capital in 2021 for $17bn! And we have many new strategic product initiatives.

We are headquartered in Boston, and our other offices are located in Atlanta, Austin, Belfast, and Burlington. In India, we have offices in Bangalore, Pune, and Chennai. Position Summary: Our Internal Developer Platform is the backbone of innovation—empowering R&D teams to build, deliver, and scale healthcare solutions with speed and confidence. We are seeking a Lead AI Solution Engineer – LMTS to join our AI Dev Tools & Solutions team in Chennai. This role focuses on transforming the developer experience through intelligent automation, seamless tooling, and AI-driven workflows.

As a technical leader, you will play a pivotal role in designing and scaling the Model Context Protocol (MCP) and orchestrating multi-agent workflows, context sharing, and intelligent automation across our internal platform. You'll collaborate with cross-functional teams to accelerate software delivery and enhance the developer experience across the organization. Qualifications & Experience:

• Proven experience building and deploying agentic AI solutions for workflow automation
• Strong programming skills in modern languages: Python, Java, TypeScript / JavaScript
• Expertise in Prompt engineering, LLMs (Large Language Models), AI-assisted development standards
• Experience with MCP servers and clients setup and management
• Experience integrating AI tools into the SDLC and evaluating their impact on productivity and quality.
• Understanding of DevOps Practices, DORA metrics and Developer productivity tooling
• Familiarity with Windsurf or similar agentic AI code editors is a strong plus.
• Excellent problem-solving skills and a proactive, ownership-driven mindset.
• Strong collaboration and communication skills to work effectively across engineering, product, and platform teams.

Key Responsibilities:

AI Infrastructure & Automation:

• Architect and implement agentic AI solutions to automate development workflows, focusing on model validation, explainability, security, and ethical AI.
• Lead the setup, configuration, and scaling of MCP servers and clients to support robust AI infrastructure.

AI-Driven SDLC Transformation:

• Design and integrate AI-powered enhancements into the Software Development Lifecycle (SDLC) to improve code quality, security, and developer productivity.
• Define and promote best practices for prompt engineering in developer tools and code editors.
• Evaluate and integrate emerging AI tools and platforms, ensuring alignment with responsible AI principles and industry standards.
• Own and optimize developer tools such as Bitbucket, SonarQube and DevOps platforms (e.g., Jenkins, GitHub Actions, Azure DevOps, etc.)

Engineering Excellence & Best Practices:

• Set and uphold high standards for code quality, performance, and maintainability.
• Lead and participate in code reviews.
• Make architectural decisions with a focus on scalability, performance, security, and developer experience.
• Maintain technical documentation for onboarding and troubleshooting.
• Integrate security best practices, including threat modeling and compliance reviews.

Innovation & Continuous Improvement:

• Champion continuous improvement in developer workflows via agentic AI, automation, and developer enablement.
• Lead the adoption of emerging trends like multi-agent systems, AI copilots, and developer-centric AI infrastructure.