Microgreen Technologies LLC

Lead AI transformation advisory and design context-aware AI architectures integrating LLMs and multi-agent systems for enterprise clients. | 10+ years AI/ML and enterprise architecture experience, 3+ years with LLMs and GenAI, expertise in context engineering tools, and strong executive advisory skills. | Role: Senior AI Architect AI, GenAI & Agentic AI (with Context Engineering Expertise) Role Overview We are looking for a Senior AI Architect to guide enterprise customers through current-state vs future-state AI transformations. The ideal candidate has deep expertise in context engineering designing and orchestrating context-aware AI systems and a proven track record of building enterprise-grade AI, GenAI, and Agentic AI solutions. This role blends strategic advisory (gap analysis, roadmaps, executive communication) with hands-on technical architecture (context pipelines, orchestration frameworks, and governance). Key Responsibilities Advisory & Gap Analysis • Conduct AI maturity and readiness assessments, covering infrastructure, data, model lifecycle, and context engineering practices. • Perform gap analysis of current-state AI/GenAI systems vs desired future-state capabilities (e.g., from single-model solutions to context-rich multi-agent ecosystems). • Recommend AI adoption roadmaps aligned with business outcomes, ROI, and compliance needs. Context Engineering & AI Enablement • Architect context engineering frameworks that unify structured, semi-structured, and unstructured enterprise knowledge into usable context for LLMs and agentic systems. • Build real-time context pipelines (ingestion, enrichment, retrieval, orchestration) to reduce latency and improve accuracy. • Define context interfaces/APIs to enable consumption of enterprise knowledge across AI agents, applications, and workflows. • Implement guardrails, policy layers, and context redaction modules to ensure security, compliance, and ethical AI usage. Enterprise-Grade AI Architecture • Design scalable architectures for AI/GenAI/Agentic AI across cloud and hybrid environments (AWS, Azure, Google Cloud Platform). • Integrate LLMs with vector databases, knowledge graphs, and real-time context stores. • Lead multi-agent system design (LangGraph, MCP, AutoGen, custom orchestration) to enable autonomous workflows. • Ensure compliance with enterprise governance frameworks (HIPAA, FDA, GDPR, Part 11/820, HITRUST). Strategy, Innovation & Customer Advisory • Run executive workshops and clearly articulate the value of context-aware AI architectures. • Build maturity models for context engineering adoption and measure business impact. • Stay ahead of the curve in Agentic AI, contextual orchestration, and self-healing AI systems. • Contribute to whitepapers, thought leadership, and reference architectures for enterprise customers. Qualifications & Experience • Education: Bachelor s/master s in computer science, AI/ML, Data Science, or related; PhD a plus. Experience: • 10+ years in AI/ML and enterprise architecture. • 3+ years with LLMs, GenAI platforms, and orchestration frameworks. • Proven ability to lead context engineering initiatives in large enterprises. Technical Expertise: • Context Engineering: pgvector, Pinecone, Redis, Weaviate, Milvus, knowledge graphs, context layering techniques. • AI/GenAI: OpenAI, Anthropic, LLaMA, Hugging Face, fine-tuning, RAG pipelines, policy/guardrail frameworks. • Agentic AI: LangChain, LangGraph, MCP, AutoGen, custom orchestration. • Enterprise Systems: Integrations with ERP, CRM, EMR/EHR, ServiceNow, etc. • MLOps & Governance: MLflow, Kubeflow, ModelOps pipelines, Responsible AI frameworks, explainability tools (SHAP, LIME). Key Competencies • Visionary strategist who can map current-state vs future-state AI for enterprises. • Hands-on architect with experience building context-aware, real-time AI ecosystems. • Strong executive advisory skills with ability to influence at CxO level. • Ability to balance innovation with compliance in regulated industries.

Develop and maintain backend data tooling and extract writing for EHR systems using SQL and cloud platform tools. | Expertise in SQL, data analysis techniques, and experience with Athena, EPIC, Oracle EHR, and Google Cloud Platform tools including BigQuery. | Athena/EPIC/Oracle EHR (Electronic Health Record) Experience with one or more of the following: database structure, developing, extract writing, backend tooling/configuration; Data analysis techniques and tools Expert; SQL Expert; Google Cloud Platform Tools including BQ