An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.
AI Engineering Lead at Coforge AI Engineering Lead at Coforge in Buffalo, New York Posted in 4 days ago.
Type:
full-time
Job Description:
Job Title:
AI Engineering Lead Skills:
AI tooling expertise must be AI savvy, Microsoft 365 CoPilot Chat and CoPilot Studio, Duo Agentic Platform or comparable agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph, or Microsoft Semantic Kernel)
Experience:
10+
Years Location:
Buffalo, New York We at Coforge are hiring AI Engineering Lead with the following skillset: 10+ years of software engineering experience, including production ownership of complex, multi-tier applications. 3+ years in a Lead Engineer or equivalent senior technical leadership role (individual contributor track). Proven hands-on experience with Microsoft 365 CoPilot Chat and CoPilot Studio, including agent design, deployment, and governance. Demonstrated experience with the Duo Agentic Platform or comparable agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph, or Microsoft Semantic Kernel). Ability to read, analyze, and reverse-engineer source code in at least one of: COBOL/JCL/CICS, Java, or equivalent enterprise language. Hands-on experience producing formal software artifacts: BRDs, sequence diagrams, architecture documentation, and requirements-to-test traceability matrices. Working knowledge of GitLab CI/CD pipeline configuration, Jira project management, and Zephyr or equivalent test management tooling. Strong understanding of software testing disciplines: unit, integration, regression, and test automation frameworks. Skills & Mindset Systems thinker who can map complex legacy architectures and translate them into structured, AI-consumable documentation. Comfort operating in ambiguity - you define the approach; you don't wait for one. Strong written and verbal communication skills; able to present technical roadmaps and AI strategies to non-technical stakeholders. Disciplined about guardrails, auditability, and responsible AI - especially in a regulated banking environment.
PREFERRED QUALIFICATIONS
Experience in a banking, financial services, or loan servicing technology environment. Familiarity with mainframe modernization patterns and the challenges of wrapping or extending
COBOL/CICS
assets in hybrid architectures.
Experience with containerized deployments:
Docker and Kubernetes in a CI/CD context. Background in prompt engineering, RAG (Retrieval-Augmented Generation), or enterprise LLM integration patterns.
Microsoft certifications:
AI-102 (Azure AI Engineer), MS-900/M365, or Power Platform/CoPilot Studio certifications. Exposure to regulatory compliance frameworks relevant to banking (e.g., SOX, FFIEC, OCC guidance on model risk and AI governance)
