Gen AI - Engineering Lead

Job description

Job Description: 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. **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 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. - 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. - 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.