Agentic AI Solution Architect & Data Analytics Director

Role:

Agentic AI Solution Architect & Data Analytics Director Location options: New York, NY or NJ Preface The Agentic AI Architect is a role within Client s AI & Data business unit in the Americas, focused on designing next-generation AI solutions that leverage autonomous agentic AI systems. These systems autonomously make decisions, take actions, adapt to changing environments, and continuously learn. Client anticipates a shift from traditional chatbots to multi-agent AI frameworks where multiple agents collaborate to determine actions. This client-facing consulting position involves shaping AI architecture across various industries, delivering vertical-specific solutions for domains like BFSI, Manufacturing, Life Sciences, Telecom, Retail, Travel, and Consumer Goods. The role involves thought leadership in emerging Business Units, ensuring Client s AI solutions are innovative, scalable, and responsibly engineered.

What You Would Be Doing Lead AI Architecture Design:

Define end-to-end architecture for AI systems incorporating autonomous agents and LLM-based components, ensuring alignment with business goals.

Client Workshops & Strategy:

Conduct workshops to understand business requirements and identify opportunities for agentic AI, translating business problems into AI architecture blueprints.

Multi-Agent Framework Orchestration:

Design frameworks for multi-agent systems, defining roles and ensuring robust communication and fail-safes.

Integration & Scalability:

Outline integration with existing enterprise ecosystems, ensuring scalability and resilience.

Leverage Prompt Engineering & RAG:

Incorporate advanced prompt engineering techniques and retrieval-augmented generation (RAG) into solution design. Technical Leadership in

Delivery:

Guide engineering teams through prototyping and solution delivery, troubleshooting high-level architectural issues.

Industry-Tailored Solutions:

Customize architectural decisions to industry-specific requirements, balancing reusability with necessary adaptations.

Emerging Tech Evaluation:

Continuously evaluate new tools and methodologies, integrating them into architecture standards.

Client Engagement & Travel:

Work closely with client technology leaders, presenting architectural proposals and reviewing technical designs, with travel as required.

Ethical & Safe Design:

Ensure ethical AI and safety considerations are embedded from the architecture stage, documenting and mitigating potential risks.

What Skills Are Expected AI/ML Solution Architecture:

Extensive experience in designing and architecting AI or machine learning solutions in an enterprise context.

Deep Technical Knowledge:

Strong understanding of machine learning and AI techniques, especially Generative AI and large language models.

Multi-Agent System Design:

Knowledge of multi-agent system patterns and frameworks.

Prompt Engineering & RAG:

Ability to craft effective prompts and chaining strategies for LLMs, familiar with retrieval-augmented generation methods.

AI Ethics & Responsible AI:

Strong grasp of AI ethics and safety principles, able to identify ethical risks and design mitigations.

Cloud & Distributed Systems:

Deep understanding of cloud architecture and distributed system design.

Data Management:

Solid understanding of data architecture as it relates to AI, including data pipelines, databases, and data lakes.

Leadership & Communication:

Excellent communication and stakeholder management skills, capable of leading discussions with C-level executives and technical brainstorming with engineers.

Consulting and Domain Acumen:

Prior consulting or client-facing experience, adept at requirement gathering and crafting proposals.

Problem-Solving & Innovation:

Creative mindset to devise innovative solutions leveraging AI agents, strong problem-solving skills.

Continuous Learning:

Demonstrated habit of continuous learning, staying updated via research papers, conferences, or hands-on experimentation. Banking, Financial Services and Insurance domain knowledge will be a plus

Key Technology Capabilities AI & ML Frameworks:

Familiarity with major AI/ML frameworks and services, including OpenAI GPT models, Google PaLM/Vertex AI, and Hugging Face Transformers library.

SaaS AI & Data Platforms:

Experience with leading SaaS AI & Data platforms in terms of agentic AI development, implementation, orchestration, AI guardrails

Agentic AI Tooling:

Exposure to frameworks and libraries for building AI agents and chains, such as LangChain ,Microsoft s Semantic Kernel.

Retrieval Systems:

Strong knowledge of search and retrieval technologies, including vector databases and semantic search.

Cloud Services:

Expertise in cloud ecosystems (AWS, Azure, Google Cloud Platform), including cloud AI services, serverless computing, containerization, and related DevOps tools.

Programming & Scripting:

Proficiency in programming languages commonly used for AI and integration, primarily Python and at least one general-purpose language.

Data Platforms:

Knowledge of modern data platforms, including relational databases, NoSQL stores, and data processing frameworks.

Integration & APIs:

Experience designing and using APIs and middleware, knowledge of event-driven architectures and message brokers.

DevOps & MLOps:

Familiar with CI/CD pipelines and infrastructure as code, understanding of MLOps principles and tools.

Security & Compliance Tools:

Comfort with technologies for securing AI applications, including identity and access management, encryption, and compliance tools.

Collaboration & Design:

Proficient with tools used in architecture and design documentation, including UML design tools and agile project management tools.

Emerging Tech:

Awareness of emerging tech such as knowledge graphs and reinforcement learning frameworks.