Solution Architect - Agentic AI & Data

 

•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, d atabases, 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.