AWS AI Platform Architect
Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Nov 1, 2026.
Description
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 AWS AI Platform Architect to lead how our clients set up the enterprise agentic stack on Amazon Web Services. In this role, you will sit at the intersection of AWS platform depth, AI platform architecture, and client advisory, as part of our AI Platform Center of Excellence.
Clients increasingly come to us wanting their entire agentic stack built on AWS: the infrastructure substrate, 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 both business users and engineers build agents. AWS provides the services. You will bring the judgment that turns them into a platform an enterprise can consume safely, at scale, and repeatably.
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 AWS-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 current AWS estate and AI operating model, and shape a clear roadmap for standing up the agentic stack on Amazon Bedrock and AgentCore. Be the person in the room who earns the client's confidence that Tiger knows AWS.
- Architect the Agentic Services Layer: Design how Bedrock and AgentCore capabilities (model access, Guardrails, Knowledge Bases, AgentCore Runtime, Gateway, Identity, Memory and Observability) are composed for enterprise consumption across accounts, covering tenancy, identity for agents, shared gateways, default guardrails, and cost attribution.
- 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 AWS setup knowledge into reference architectures, recipes, golden paths, and reusable kits that our largely platform-agnostic team can deploy repeatedly.
- Support Internal AI Applications: Advise on Tiger's internal AI applications running on AWS, improving their architecture, operations, and cost.
Who do we expect?
- Experience: Typically 10 to 12 years in technology, with at least 4 to 6 years of hands-on, enterprise-scale work on AWS, including 2 to 3 years delivering on Amazon SageMaker, Amazon Bedrock, or AWS's broader ML and data services.
- AWS Platform Depth: Strong command of how enterprises run AWS: multi-account strategy with AWS Organizations and Control Tower, IAM and service control policies, VPC and PrivateLink networking, EKS, ECS and Lambda, and data governance with Lake Formation or equivalent.
- AI Platform Architecture: Proven experience architecting ML or GenAI platforms on AWS 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 Amazon Bedrock AgentCore and Bedrock Agents. You have built, deployed, and formed opinions on agents on AWS, even if not yet at production scale.
- IaC and Automation Literacy: Understands how platform setup is expressed in Terraform, AWS CDK or CloudFormation, and can review IaC for architectural intent. You will not be expected to write it; our infrastructure engineers will.
- Certifications: AWS Certified Solutions Architect - Professional, plus AWS Certified Generative AI Developer - Professional or Machine Learning Engineer - Associate. A held Machine Learning - Specialty certification is also valued.
- Client Presence: Comfortable leading technical conversations with client architects and senior technology leaders, translating platform choices into business trade-offs.
- Consulting Mindset: Comfortable being staffed on billable engagements and splitting time across advisory, delivery, and internal asset building.