Generative AI Engineering Lead

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.

• 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. 
• Hands-on experience with GCP, and Snowflake AI ecosystems. 
• 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.

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 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 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, SageMaker, 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.