Senior AI Engineer

CGI•Bloomfield, CT

5d•Hybrid

About The Position

The best version of us starts with You! We CGI is seeking a highly skilled and experienced Senior AI Engineer to join our Health Care client in one of our selected client locations. This is an exciting opportunity to work in a fast-paced team environment supporting one of the largest leaders in the Health Care industry. This Senior AI Engineer will be instrumental to design, develop, and lead enterprise-grade Generative AI solutions. This role requires deep hands-on expertise in Python, Large Language Models (LLMs), prompt engineering, embeddings, vector databases, and agent-based architectures. The role will work closely with the AI Center of Excellence (AI CoE) to ensure alignment with enterprise AI standards, governance, and best practices while driving adoption across delivery teams. This position can be performed from any Client site Bloomfield, CT, Raleigh, NC or Lafayette, LA in a Hybrid working Model.

Requirements

At least 7+ years of professional software engineering experience, with strong hands-on expertise in LangChain, Python building scalable, production-grade applications.
Strong experience developing GenAI applications using LangChain and implementing observability, evaluation, and debugging using LangSmith or equivalent tools.
Demonstrated experience delivering enterprise Generative AI solutions, including design and implementation of RAG, agentic, and multi-agent architectures.
Proven hands-on experience with LLMs, including prompt engineering, optimization, evaluation, and lifecycle management in production environments.
Experience designing and managing embeddings and vector-based retrieval pipelines using industry-standard vector databases or search platforms.
Experience integrating GenAI solutions with enterprise platforms, APIs, and data sources, including structured data, documents, and unstructured content.
Hands-on experience implementing tool/function calling, memory, and agent orchestration to support complex workflows and automation use cases.
Experience working with multimodal AI solutions involving text, documents, and images.
Proven ability to evaluate, select, and operationalize LLMs (e.g., OpenAI, Azure OpenAI, open-source) with a focus on performance, scalability, cost, and reliability.
Experience collaborating with an AI Center of Excellence (AI CoE) to align on enterprise standards, reference architectures, and reusable components.
Strong understanding of AI governance, security, responsible AI, and compliance requirements in regulated enterprise environments.
Demonstrated experience leading and mentoring engineering teams, reviewing designs, and establishing repeatable patterns and best practices.
Ability to quickly assess emerging GenAI technologies and apply them pragmatically within enterprise constraints.
Proactive approach to architectural simplification, reuse, and optimization to drive scalability and long-term maintainability.
Ownership mentality, with the ability to lead initiatives end-to-end, from solution design through production deployment and adoption.
Commitment to driving consistency, quality, and governance across GenAI implementations through collaboration with delivery teams and the AI CoE.

Responsibilities

Technical Leadership & Architecture
Lead the design and development of scalable Generative AI solutions using LLMs
Define and implement architectures for RAG, agentic, multi-agent, and multimodal systems
Review and guide solution designs to ensure alignment with AI CoE standards and enterprise architecture
Mentor and guide developers on GenAI patterns, tools, and best practices
Development & Engineering
Develop end-to-end AI solutions using Python
Design, test, and optimize prompt engineering strategies
Build and manage embeddings, vector search, and semantic retrieval pipelines
Develop applications using LangChain and monitor, evaluate, and debug workflows using LangSmith
Integrate LLMs with enterprise systems, APIs, and structured/unstructured data sources
Implement agent-based and multi-agent workflows for task orchestration and automation
Work with multimodal models involving text, documents, and images
Collaboration with AI CoE
Work closely with the AI CoE to align on AI frameworks, reusable components, and architectural standards
Contribute to enterprise GenAI accelerators, reference architectures, and best practices
Support governance, security, responsible AI, and compliance requirements
Share learnings, patterns, and improvements across teams to drive consistency and adoption
AI & LLM Expertise
Apply deep understanding of LLM capabilities, limitations, and optimization techniques
Implement techniques such as RAG, tool/function calling, memory, and agents
Evaluate and recommend appropriate models (OpenAI, Azure OpenAI, open-source models)
Ensure performance, scalability, cost optimization, and reliability of AI solutions