Solution Architect - Agentic AI & Data - BFSI

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.


< div>•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, GCP), 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.