Gen AI - Engineering Lead

EXL•Hartford, CT

About The Position

We are seeking a highly skilled Senior Generative AI Engineer Lead to drive the design, development, and deployment of enterprise-grade Generative AI solutions. The role requires deep expertise in Large Language Models (LLMs), prompt engineering, AI orchestration frameworks, cloud-native AI architectures, and model evaluation methodologies. The position leads the end-to-end lifecycle of GenAI initiatives, from translating business requirements into AI-powered prototypes to delivering scalable, production-ready solutions across AWS, GCP, and Snowflake ecosystems. It also establishes engineering best practices for prompt governance, model guardrails, benchmarking, and performance optimization.

Requirements

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
8+ years of experience in software engineering, machine learning, data engineering, or AI solution development.
4+ years of hands-on experience with Generative AI, LLMs, and foundation models.
Strong experience designing enterprise GenAI solutions using advanced LLM architectures and prompt frameworks.
Expertise in building scalable AI workflows, orchestration pipelines, and reusable AI components.
Proven ability to translate business requirements into production-ready AI solutions and prototypes using synthetic data.
Deep understanding of prompt versioning, prompt governance, guardrails, and controlled LLM outputs.
Strong knowledge of AI evaluation frameworks, benchmarking methodologies, and optimization techniques.
Experience with vector databases, embeddings, semantic search, and RAG architectures.
Strong proficiency in Python and modern AI/ML development frameworks.

Responsibilities

Architect and implement enterprise-scale GenAI solutions using LLMs, foundation models, and agentic AI frameworks.
Design reusable AI patterns, accelerators, and reference architectures to enable rapid solution development.
Translate business problems into scalable AI workflows and production-ready proof-of-concepts.
Drive AI platform modernization through adoption of emerging GenAI technologies and best practices.
Develop and maintain prompt engineering frameworks for controlled and reliable LLM outputs.
Implement prompt versioning, prompt lifecycle management, and testing strategies.
Design AI guardrails to mitigate hallucinations, bias, security risks, and compliance concerns.
Establish best practices for prompt optimization, response consistency, and output quality management.
Design and build scalable orchestration pipelines using frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, or equivalent.
Develop reusable AI components, tools, agents, and workflow templates for enterprise adoption.
Implement multi-agent systems and autonomous workflows for complex business use cases.
Partner with business stakeholders to identify high-value AI opportunities.
Develop AI prototypes and MVP solutions using synthetic and enterprise datasets.
Convert prototypes into production-ready applications that meet scalability, security, and reliability standards.
Build scalable GenAI architectures across AWS, GCP, and Snowflake platforms.
Leverage cloud-native AI services including Amazon Bedrock, SageMaker, Vertex AI, Snowflake Cortex AI, and related ecosystems.
Design RAG architectures incorporating vector databases, embeddings, and semantic search.
Optimize model deployment, inference performance, and infrastructure cost efficiency.
Establish AI evaluation frameworks to measure accuracy, relevance, latency, safety, and business impact.
Develop benchmarking methodologies for comparing prompts, models, and workflows.
Define KPIs, observability frameworks, and monitoring strategies for GenAI applications.
Continuously improve model performance through prompt tuning, retrieval optimization, and workflow enhancements.