Snowflake AI Platform Architect-Data&AI

Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow.

Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence.

We are a Great Place to Work-Certified™ (2022-24), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine.

Curious about the role?

We are seeking a highly technical and hands-on Databricks AI Platform Architect to lead how our clients set up the enterprise agentic stack on the Databricks Data Intelligence Platform. In this role, you will sit at the intersection of Databricks platform depth, AI platform architecture, and client advisory, as part of our AI Platform Center of Excellence.

Many clients now ask us to build their agentic stack on Databricks first, and to reach for their cloud provider's services only where Databricks does not cover the need. That covers the agentic services layer (LLM gateway, agent and tool/MCP registries, evals and guardrails, data connectors, runtime observability, Agent/LLM Ops and FinOps) and the experience layer through which business users and engineers build agents. You will bring the judgment that turns Databricks' capabilities into a governed platform an enterprise can consume safely, at scale, and repeatably, and you will know precisely where the Databricks boundary sits.

This is a senior individual contributor role. You will work as a peer to our AI Architects, with a clear path to leading a small pool of Databricks-specialist AI platform engineers as the practice grows.

As part of this role, you will:

• Lead Client Advisory and Pre-Sales: Assess a client's Databricks estate and AI operating model, and shape a clear roadmap for standing up the agentic stack on Databricks. Be the person in the room who earns the client's confidence that Tiger knows Databricks.

• Architect the Agentic Services Layer: Design how Mosaic AI and Agent Bricks capabilities (Agent Framework, AI Gateway, Model Serving, Vector Search, MLflow, Lakehouse Monitoring, and MCP hosting through Databricks Apps) are composed for enterprise consumption under Unity Catalog, covering workspace topology, identity for agents, shared gateways, default guardrails, and cost attribution.

• Define the Platform Boundary: Identify where Databricks fully covers the client's needs and where the host cloud's services are a better fit. Work with our GCP and AWS AI Platform Architects, and our Azure specialists, to integrate those services without breaking governance.

• Guide Delivery to the Promise: Advise delivery teams through implementation so what was designed is what gets built. Review architectures, IaC intent, and configurations, and resolve hard platform questions as they arise.

• Build Golden Paths and Kits: Codify Databricks setup knowledge into reference architectures, recipes, golden paths, and reusable kits that our largely platform-agnostic team can deploy repeatedly.

Who do we expect?

• Experience: Typically 10 to 12 years in technology, with at least 3 to 5 years of hands-on, enterprise-scale work on Databricks, ideally including deployments in the Unity Catalog era, and 2 to 3 years delivering ML or GenAI workloads on the platform.

• Databricks Platform Depth: Strong command of how enterprises run Databricks: account and workspace design, Unity Catalog metastores and governance, networking and private connectivity, serverless compute, Delta Lake, Lakeflow, and cost management.

• AI Platform Architecture: Proven experience architecting ML or GenAI platforms on Databricks for shared enterprise use, not only individual applications. Understands model serving, gateways, evaluation, monitoring, and governance as platform capabilities.

• Agentic Services Exposure: Hands-on exploration of Agent Bricks and the Mosaic AI Agent Framework. You have built, deployed, and formed opinions on agents on Databricks, even if not yet at production scale.

• Host Cloud Awareness: A working understanding of how Databricks runs on Azure, AWS and GCP, enough to recognise when a need falls outside Databricks and bring in the right specialist.

• IaC and Automation Literacy: Understands how Databricks setup is expressed through the Terraform provider and Databricks Asset Bundles, and can review it for architectural intent. You will not be expected to write it; our infrastructure engineers will.

• Certifications: Databricks Certified Data Engineer Professional or Machine Learning Professional, plus Generative AI Engineer Associate.

• Client Presence and Consulting Mindset: Comfortable leading technical conversations with client architects and senior technology leaders, being staffed on billable engagements, and splitting time across advisory, delivery, and internal asset building.