Gen AI - Engineering Lead

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.