Role: GEN AI Architect
Location: NYC highly preferred Onsite
Full Time
Key Responsibilities:
- Client Advisory & Solutioning: Engage directly with senior client stakeholders (including C-suite) to understand complex business challenges, identify opportunities for GenAI and Agentic AI, and define project scope.
- Workshop Facilitation: Design, lead, and facilitate high-impact client workshops and strategy sessions focused on identifying and prioritizing Generative and Agentic AI use cases and roadmap development.
- Technical Leadership & Architecture: Design, architect, and oversee the development and deployment of scalable, robust, and cutting-edge Generative AI and sophisticated Agentic AI systems (including multi-agent workflows) for client and internal projects.
- Project & Engagement Leadership: Lead large-scale, complex Generative AI and Agentic AI projects from strategic conception through successful deployment, managing cross-functional teams (internal and client-side) and ensuring timely delivery of high-quality solutions.
- Technical Mentorship: Mentor and guide technical teams (data scientists, engineers) in best practices for advanced AI development, deployment, MLOps/LLMOps, and agentic system design.
- Stakeholder Management: Build and maintain strong relationships with key internal and external stakeholders, effectively communicating complex technical concepts and project progress.
- Quality & Best Practices: Ensure adherence to rigorous software engineering principles, Agile methodologies, and responsible AI practices throughout the solution lifecycle.
- Stay Current: Maintain deep expertise in the latest trends, research, tools, and technologies within Generative AI, Large Language Models (LLMs), and Agentic AI paradigms.
Technical Skills:
- Programming & Libraries: Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK).
- LLM Expertise: Proven experience developing applications leveraging state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude) including prompt engineering, fine-tuning, and evaluation.
- GenAI & Agentic Frameworks: Hands-on mastery of core GenAI frameworks (e.g., LangChain, LlamaIndex) and practical experience with Agentic AI frameworks and concepts (e.g., AutoGen, CrewAI, LangGraph, agent planning, tool use integration, multi-agent collaboration).
- AI Architecture: Deep understanding of AI/ML system architecture patterns, including microservices, event-driven architectures, and patterns specific to RAG (Retrieval-Augmented Generation), Graph RAG, Agentic RAG, and multi-agent systems.
- Vector Databases & Embeddings: Expertise in working with various embedding models and vector databases (e.g., Pinecone, Weaviate, Chroma, FAISS).
- Advanced AI Concepts: Strong grasp of advanced techniques such as complex task decomposition for agents, reasoning engines, knowledge graphs, autonomous agent design, and evaluation methodologies for complex AI systems.
- Software Engineering: Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems.
- Cloud Platforms: Strong working knowledge and practical deployment experience on at least one major cloud platform (AWS, Azure, Google Cloud Platform), including their AI/ML services.
- LLMOps/MLOps: Expertise in designing and implementing robust MLOps/LLMOps pipelines for automated testing, CI/CD, monitoring, and governance of complex AI models and applications.
Leadership & Communication:
- Proven ability to lead and motivate diverse, global teams
- Excellent communication skills, capable of explaining complex AI concepts to various stakeholders
- Strong project and program management skills and experience working in Agile environments
Qualifications:
- Education: Bachelor's degree in Computer Science, AI, Machine Learning, or a related quantitative field. Master's or Ph.D. strongly preferred.
- Experience: Minimum 10 years of experience in AI/ML/Data Science, with at least 5 years in significant leadership roles involving solution architecture, team management, and project delivery.
- Deployment Success: Demonstrated track record of successfully architecting and deploying large-scale AI projects, preferably including complex GenAI and/or Agentic AI applications in enterprise or client settings.
- Consulting Background: Prior experience in technology consulting or a client-facing technical specialist role within a technology provider is highly advantageous.
- Global Experience: Experience working effectively with global teams across multiple geographic locations is a plus.
We are looking for a seasoned Senior Principal Architect to spearhead end to end assessments for Enterprise scale AI and Intelligent Automation initiatives for a public sector client. Operating as a trusted advisor to the executives, you will diagnose current state capabilities, craft future state architectures, and build pragmatic transformation roadmaps that unlock measurable value from Generative AI, machine learning, RPA, and intelligent workflow orchestration. You will serve as the critical link between executive vision, technology strategy, and delivery execution-balancing quick wins with long term platform resilience.
Key Responsibilities
Executive Discovery & Assessment
- Lead C suite workshops to capture business objectives, automation pain points, and AI ambitions.
- Conduct rapid maturity assessments using TOGAF/SAFe, AI Capability Maturity Models, and industry benchmarks.
- Quantify value pools, ROI, and risk for prioritized use cases (e.g., GenAI copilots, intelligent document processing, autonomous workflows).
Target Architecture & Roadmap
- Define end to end architectures covering data, application, integration, security, and governance layers-cloud agnostic but fluent in AWS, Azure, and Google Cloud Platform native AI services.
- Establish reference patterns for LLM orchestration, RAG pipelines, agent based automation, and event driven microservices.
Governance & Operating Model
- Design AI/Automation Centers of Excellence, policy guardrails, and federated governance models (data privacy, ethics, and compliance).
- Recommend platform selection criteria (build vs. buy) and partner ecosystems.
Proofs of Value & Quick Win Delivery
- Oversee rapid proofs of value to validate assumptions, de risk technology choices, and demonstrate tangible impact within 90 days.
Thought Leadership & Capability Building
- Mentor client and internal teams; publish POVs on emerging AI trends, DevOps for ML, and responsible AI frameworks.
- Contribute reusable blueprints, assessment accelerators, and methodology assets across the consulting practice.
Qualifications
- Education: Bachelor's in Computer Science, Engineering, or related field (Master's or PhD preferred).
- Experience: 15+ years in enterprise / solution architecture, with 8+ years leading AI, automation, or digital transformation programs in a consulting environment.
- Technical Depth:
- Hands on architecture for GenAI, LLM fine tuning/RAG, ML Ops, RPA, event streaming, and API first integration.
- Fluency across major hyperscaler stacks (AWS SageMaker & Bedrock, Google Vertex AI & Duet, Azure OpenAI & ML).
- Working knowledge of security, data governance, and DevSecOps best practices.
- Methodology & Frameworks: TOGAF 9/10, Zachman, SAFe, ITIL 4, COBIT, or equivalent; Lean/Agile delivery.
- Business Acumen: Proven ability to translate executive vision into actionable technology roadmaps and to build multimillion dollar business cases.
- Soft Skills: Polished communicator with board room presence; excels at stakeholder alignment, conflict resolution, and cross functional leadership.
Certifications (nice to have): TOGAF, AWS/Azure/Google Cloud Platform Architect Professional, PMP, Certified RPA Architect (UiPath/Automation Anywhere), or equivalent.
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