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.
