Enterprise Generative AI Engineer Needed
Budget: $700 – $800 USD
Seeking a senior Generative AI Engineer to drive enterprise-wide AI initiatives within an AI Center of Excellence.
Role focuses on building GenAI and Agentic AI solutions, including RAG pipelines, AI agents, and workflow automation across platforms like AWS, Microsoft 365, and Copilot.
Requires 10+ years in software engineering, with 2–3 years hands-on in Generative AI, strong Python skills, and cloud experience (preferably AWS).
Core responsibilities:
Design and develop GenAI solutions using prompt engineering, context engineering, and RAG
Build interoperable AI agents using MCP / Google A2A
Automate extraction of unstructured data (emails, PDFs, web) into structured outputs
Develop reusable enterprise AI services and APIs
Implement LLM testing, monitoring, and security practices
Key skills:
Strong experience with LLM APIs (OpenAI, Azure OpenAI, Gemini, Anthropic)
Expertise in RAG (chunking, embeddings, retrieval)
Experience with REST APIs, OCR/document processing, and observability tools
Deep understanding of AI governance, security, and data privacy
Ideal candidate:
A self-driven builder and problem solver who can turn ambiguous business needs into scalable AI systems
Comfortable working across multiple tools, platforms, and teams while aligning with enterprise standards.
Role focuses on building GenAI and Agentic AI solutions, including RAG pipelines, AI agents, and workflow automation across platforms like AWS, Microsoft 365, and Copilot.
Requires 10+ years in software engineering, with 2–3 years hands-on in Generative AI, strong Python skills, and cloud experience (preferably AWS).
Core responsibilities:
Design and develop GenAI solutions using prompt engineering, context engineering, and RAG
Build interoperable AI agents using MCP / Google A2A
Automate extraction of unstructured data (emails, PDFs, web) into structured outputs
Develop reusable enterprise AI services and APIs
Implement LLM testing, monitoring, and security practices
Key skills:
Strong experience with LLM APIs (OpenAI, Azure OpenAI, Gemini, Anthropic)
Expertise in RAG (chunking, embeddings, retrieval)
Experience with REST APIs, OCR/document processing, and observability tools
Deep understanding of AI governance, security, and data privacy
Ideal candidate:
A self-driven builder and problem solver who can turn ambiguous business needs into scalable AI systems
Comfortable working across multiple tools, platforms, and teams while aligning with enterprise standards.