Solution Architect - Agentic AI & Data - BFSI

Location: Edison, NJ
Salary: $ USD Annually - $ USD Annually
Description:
AI & Data Architects

Location- NY/NJ
, Santa Clara (Bay Area), Dallas TX

Fulltime

Job Description:

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.

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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 Lang Chain ,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 Dev Ops tools.

Programming & Scripting:
Proficiency in programming languages commonly used for AI and integration, primarily Python and at…