Gen AI - Engineering Lead

Job-:
Data Analytics

Location:

Hartford, Connecticut, United States Workplace:
Hybrid Posted:

Job Role:

Gen AI Engineer-Gen AI Engineer Experience (In Years): 6-9

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 ideal candidate will have deep expertise in Large Language Models (LLMs), prompt engineering, AI orchestration frameworks, cloud-native AI architectures, and model evaluation methodologies.

This role will lead 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. The candidate will also establish engineering best practices around prompt governance, model guardrails, benchmarking, and performance optimization.

Qualifications 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 utilizing 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 Generative AI Solution Design 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.

Prompt Engineering & LLM Governance Develop and maintain sophisticated 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.

AI Workflow Orchestration & Automation Design and build scalable orchestration pipelines using frameworks such as Lang Graph, Lang Chain, CrewAI, Semantic Kernel, or equivalent.

Develop reusable AI components, tools, agents, and workflow templates for enterprise adoption.

Implement multi-agent systems and autonomous workflows to support complex business use cases.

Prototyping & Business Enablement Partner with business stakeholders to identify high-value AI opportunities.

Rapidly develop AI prototypes and MVP solutions using synthetic and enterprise datasets.

Convert prototypes into production-ready applications adhering to scalability, security, and reliability standards.

Cloud & Data Engineering Build scalable GenAI architectures across AWS, GCP, and Snowflake platforms.

Leverage cloud-native AI services including Amazon Bedrock, Sage Maker, Vertex AI, Snowflake Cortex AI, and related ecosystems.

Design robust RAG (Retrieval-Augmented Generation) architectures incorporating vector databases, embeddings, and semantic search.

Optimize model deployment, inference performance, and infrastructure cost efficiency.

Evaluation & Performance Optimization 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.