Lead Engineer – Context Engineering (AI Platform)
- Location
- Across Remote
- Salary
- Not specified
- Company
- Akash pawar
Job description
Role & Company / Project Overview: Urgent hiring for a Lead Engineer – Context Engineering to establish and scale enterprise Context Engineering capabilities for AI agents, copilots, conversational systems, and intelligent workflows at El Shaddai Technologies. The role focuses on AI memory, context management, RAG, knowledge grounding, personalization, retrieval systems, and contextual state management across enterprise AI platforms. Key Responsibilities & Daily Duties: • Establish Context Engineering as an enterprise AI discipline. • Design short-term, long-term, episodic, semantic & task-specific memory. • Build context orchestration, persistence and retrieval frameworks. • Design RAG & Knowledge Grounding architectures. • Develop context ranking, prioritization, filtering & compression strategies. • Enable AI Agent / Multi-Agent memory and context sharing. • Build reusable Context APIs, SDKs and platform services. • Design scalable, cloud-native AI platform capabilities. • Implement observability for context quality, retrieval and AI performance. • Establish AI memory security, governance, privacy and compliance. • Mentor engineers and drive enterprise AI platform strategy. Required Qualifications, Skills & Experience: • 12+ years of overall industry experience (10+ years Software Engineering). • 4+ years dedicated experience in AI/ML / Generative AI / Intelligent Automation. • Strong Azure Cloud-native platform experience. • Deep expertise in LLMs, RAG, Vector Databases & Semantic Search. • Hands-on experience with LangChain / LangGraph / Semantic Kernel / LlamaIndex / CrewAI / AutoGen or similar frameworks. • Strong programming proficiency in Python, Java, or C#. • Deep understanding of microservices, APIs, and event-driven architecture. • Solid knowledge of AI context and memory management, enterprise data, metadata, and knowledge management. • Familiarity with enterprise security, privacy, and AI governance. • Preferred / Good to Have: Agentic AI & Multi-Agent Systems, MCP (Model Context Protocol), Pinecone / Weaviate / Chroma / Qdrant / Azure AI Search, Knowledge Graphs / Graph Databases / Semantic Modeling, Prompt Engineering & AI Evaluation Frameworks, Responsible AI, Financial Services / Wealth Management / Insurance / Benefits domain experience, Enterprise-wide AI Platform leadership. Success Measures: • Improved AI accuracy, relevance & personalization. • Reduced hallucinations through contextual grounding. • Persistent cross-session AI experiences. • Reusable context services accelerating AI Agent & Copilot development. • Scalable Context Engineering infrastructure for enterprise AI adoption. Working Hours, Shifts & Location Details: • Location: Fully Remote. Application Instructions: • Share relevant profiles with updated CV and contact details ASAP.