Principal Gen AI Engineer (Full Stack)

We are

At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron’s progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 16,850+, and has 60 offices in 20 countries within key global markets.

Our challenge

We are seeking a highly skilled Principal AI Engineer to lead the design, development, and scaling of Agentic and Generative AI solutions. This role is hands-on, strategic, and pivotal in driving complex initiatives, establishing technical standards, and mentoring engineering teams to ensure excellence.

Additional Information*

The base salary for this position will vary based on geography and other factors. In accordance with law, the base salary for this role if filled within Charlotte, NC / Iselin, NJ is $130k - $134k/year & benefits (see below).

The Role

Responsibilities:

• Lead or contribute to complex AI/ML initiatives, ensuring high-quality deliverables within software engineering environments.
• Develop and execute large-scale strategies for Generative AI capabilities, including platform architecture, engineering standards, and operational readiness.
• Design, develop, test, debug, and document AI-enabled services and applications, managing deployments across various environments.
• Architect and implement Generative AI solutions utilizing large language models (LLMs), retrieval-augmented generation (RAG), and agentic frameworks; integrate these capabilities into existing enterprise systems.
• Build production-grade agentic solutions leveraging LangChain, LangGraph, and other relevant frameworks, combining rapid composition with explicit state-machine orchestration.
• Develop multi-agent systems using Google ADK, harnessing its architecture, tool ecosystem (including MCP tools), evaluation, and deployment patterns.
• Engineer secure tool execution methods, ensuring best practices in service-to-service communication, least privilege, auditability, and enterprise standards.
• Assess and resolve moderately complex technical challenges related to AI technologies, orchestration, model behavior, and integration patterns.
• Troubleshoot and resolve issues impacting AI services and models, ensuring monitoring, operational hygiene, and risk mitigation.
• Lead the development and implementation of lifecycle tools and utilities, including evaluation metrics, quality monitoring, and drift detection.
• Collaborate with peers and stakeholders, providing guidance to less experienced team members, and promoting engineering best practices.
• Serve as a technical escalation point, driving excellence and consistency across the team.
• Ensure all engineering efforts comply with policies, procedures, security protocols, and risk management requirements.

Requirements:

• Proven experience in designing and deploying large-scale Generative AI and agentic solutions.
• Strong expertise with LLMs, RAG, LangChain, LangGraph, and multi-agent architectures.
• Hands-on experience with Google ADK and multi-agent orchestration frameworks.
• Solid understanding of secure API development, service-to-service communication, and enterprise security standards.
• Excellent problem-solving skills with the ability to evaluate complex technical challenges.
• Demonstrated leadership capabilities, mentoring skills, and experience guiding engineering teams.
• Experience with AI lifecycle management, including evaluation, monitoring, and model drift detection.