Job Description Summary

Job Description Summary
As a Staff AI Engineer, you’ll lead enterprise full stack and AI platform architecture at scale for multiple initiatives across the company. You’ll design and ship microservices and REST APIs, build RAG and multimodal LLM features, and set standards for AI-assisted development. You’ll make technical decisions end-to-end, balancing reliability, performance, and cloud cost for AI systems.
You will partner with globally distributed teams to engage, intervene and accelerate assimilation of AI into Grid SW services activities that drive tangible outcomes.

Job Description

Key Responsibilities:

• Identify and deliver reusable AI components for forward-deployed engineers.
• Ship resilient REST APIs and microservices.
• Build RAG and multimodal LLM features.
• Design agentic workflows for autonomous tasks.
• Mentor engineers and provide guidance to contractors and partners.

Required Qualifications:

• Bachelor’s degree in computer science, Computer Engineering, or a related technical field.
• 10+ years of experience with at least one general purpose programming language (Python [preferred], Java, or Go) and front-end experience with JavaScript/TypeScript.
• Proven track record shipping production applications that incorporate LLMs or AI services at scale.
• Demonstrated experience leading technical teams and providing guidance to external contractors or vendors.
• Demonstrated experience of driving at least 5 projects with strong AI & ML engagement
• Strong experience with cloud platforms (AWS or Azure) and advanced containerization (Docker, Kubernetes).
• Experience with technical oversight, code review processes, and quality assurance methodologies.
• Strong trouble shooting skills
• Good communication skills and prior experience of working with globally distributed teams​

Preferred Qualifications:

• Experience in AI/ML, including architectural design, technical leadership, and system optimization.
• Having a master's degree or equivalent in AI is a plus.
• Experience with modern integration frameworks (SOAP, Rest, MuleSoft, ) and advanced state management.
• Experience with vector databases (e.g. Milvus, Pinecone, Weaviate, pgvector) and embedding model optimization.
• Experience with multiple LLM providers and advanced AI-orchestration frameworks (e.g. LangChain, LlamaIndex).
• Experience with enterprise RAG architectures, semantic search optimization, and conversation memory management at scale.
• Experience building and scaling AI-powered features for 100k+ users.

Additional Information