Solution Architect - Agentic AI & Data

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.

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.

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.

Qualifications

• BACHELOR OF COMPUTER SCIENCE