About ARA's Client Global AI & Data Transformation Leader :

ARA's client is a prominent global professional services company specializing in digital, cloud, and security. They leverage advanced technologies like AI and data analytics to drive innovation and solve complex business challenges for their diverse client base. With a strong commitment to delivering transformative solutions, ARA's Client empowers organizations across various industries to achieve their strategic objectives and maintain a competitive edge.

Role Summary :

ARA's Client is seeking a seasoned Snowflake Data Warehouse Manager to architect and lead the development of cutting-edge AI platform architectures built on Snowflake. This role is pivotal in translating business strategy into technical vision, ensuring the successful design and delivery of modern AI systems for their clients.

Key Responsibilities :

- Define Snowflake-native AI application architecture, including Cortex-based agents, RAG, and document intelligence patterns.

- Establish Snowpark and Streamlit application patterns, ensuring robust RBAC, masking, lineage, and cost controls for GenAI workloads.

- Architect model and tool agnostic multi-agent systems, encompassing orchestration, tool use, agent memory, and context management.

- Design the end-to-end data and context layer, covering ingestion, preprocessing, vector search, knowledge graphs, and semantic retrieval for RAG.

- Lead stakeholder workshops to align on project feasibility, scope, solution boundaries, and client-facing expectations.

- Translate business strategy and product goals into a technical vision, architecture blueprint, and implementation roadmap.

- Define evaluation frameworks for key AI performance metrics such as accuracy, relevance, faithfulness, latency, and cost.

- Establish AI security, governance, and observability as central design controls, including guardrails, prompt injection defense, and PII protection.

- Maintain architecture decision records, component diagrams, sequence diagrams, and reusable reference architecture assets.

- Bring practical industry experience (e.g., banking, healthcare, retail) to shape domain-grounded solutions and ensure alignment with enterprise standards.

Must-Have Qualifications :

- 12+ years of experience in software engineering, data engineering, AI/ML engineering, or technology architecture.

- 5+ years experience designing/deploying enterprise-grade advanced AI or cloud data solutions, with at least one cloud vendor.

- 2+ years of experience in agentic AI, LLM, and generative AI solution architecture or engineering delivery.

- Hands-on architecture and engineering experience with Snowflake Cortex AI, Cortex Agents, Snowpark, and Streamlit.

- Strong knowledge of LLM architecture patterns including RAG, embeddings, vector databases, prompt engineering, and agent orchestration.

- Proficiency in Python coding (4+ years experience) and experience with APIs, distributed systems, and cloud-native application patterns.

- Demonstrated experience as a solution/technology architect in industry contexts such as banking, insurance, retail, healthcare, or telecom.

- Bachelor's degree or equivalent in Computer Science, Data Science, AI/ML, or a related engineering discipline.

Nice to Have :

- SnowPro Advanced Architect, SnowPro Advanced Data Engineer, or Snowflake ML exposure.

- Experience with open-source AI and orchestration frameworks (LangChain, LlamaIndex, MLflow, FastAPI).

- Exposure to responsible AI, model risk management, AI governance boards, and GenAI FinOps.

- Experience building reusable enterprise reference architectures, estimation models, and playbooks.