AWS AI Platform Strategist (Remote)

Cognizant; Boston (MA); Remote; USD 80,000 - 180,000; Full time

Job summary

Cognizant is seeking an AI Platform Strategist to define and drive enterprise AI platform vision, strategy, and AWS-based roadmaps. You will bridge business leadership, data teams, cloud engineering, and AI practitioners to build a scalable, governed AI ecosystem that accelerates value creation.

You will lead architecture, governance, and capability development across services like Bedrock, SageMaker, OpenSearch, and MLOps, ensuring secure, compliant, and reusable patterns.

Qualifications

• 10+ years of experience in enterprise architecture, cloud strategy, data strategy, AI strategy, or digital transformation.
• 5+ years working with AWS cloud technologies.
• Experience designing enterprise AI or data platforms.
• Strong understanding of Generative AI, Agentic AI, Machine Learning, Data Platforms, MLOps and LLMOps.
• Experience developing executive-level roadmaps and business cases.
• Strong communication and stakeholder management skills.

Responsibilities

• Define the enterprise AI platform vision and target operating model.
• Develop multi-year AI platform roadmaps aligned to business and technology objectives.
• Assess current AI, data, analytics, and cloud capabilities.
• Identify platform gaps, dependencies, and modernization priorities.
• Establish phased implementation plans and investment recommendations.
• Define target-state AI platform architecture leveraging AWS services.
• Develop architecture patterns for Generative AI, Agentic AI, ML, Data Science Workbenches, and Model Lifecycle Management.
• Establish design principles for scalability, interoperability, security, and reusability.
• Guide integration of AI capabilities with enterprise applications and business workflows.
• Lead strategy for platform capabilities including Bedrock, SageMaker, Glue, EMR, Redshift, OpenSearch, Kendra, EKS, Lambda, Step Functions.
• Define platform services such as Prompt Management, Model Catalogs, Feature Stores, Vector Databases, RAG Frameworks, Agent Orchestration, MLOps, LLMOps, API Management, AI Monitoring.
• Define AI governance frameworks and operational controls.
• Establish model governance and lifecycle management processes.
• Create policies for Responsible AI, Data Privacy, Security, Compliance, Risk Management.
• Define AI guardrails and human-in-the-loop review processes.
• Align platform capabilities with industry and regulatory requirements.
• Design intake, assessment, and prioritization frameworks for AI opportunities.
• Create value-based scoring methodologies.
• Align business demand with platform capabilities.
• Develop repeatable processes for onboarding new AI use cases.
• Maintain enterprise AI opportunity portfolios and roadmaps.
• Define AI platform ownership and organizational structures.
• Establish AI Centers of Excellence (CoE).
• Develop platform support and service models.
• Recommend talent, skills, and capability-building strategies.
• Drive adoption and change management initiatives.
• Advise executive stakeholders on AI platform investments and priorities.
• Present AI strategy, architecture, and roadmap recommendations.
• Develop board-level and executive business cases.
• Align platform initiatives to measurable business outcomes and ROI.

Skills

Enterprise architecture

AWS cloud technologies

AI strategy

Communication & Stakeholders

Executive roadmaps & business cases

Cloud-native architectures

Job description

Position Summary

The AI Platform Strategist is responsible for defining and driving the enterprise AI platform vision, strategy, and roadmap on AWS. This role serves as the bridge between business leadership, data teams, cloud engineering, architecture, and AI practitioners to design a scalable, secure, and governed AI ecosystem that accelerates innovation and business value.

The strategist leads platform planning activities, assesses current-state AI, data, analytics, and cloud capabilities, defines future-state architectures, establishes governance frameworks, and develops implementation roadmaps for AI, Generative AI, Agentic AI, and advanced analytics solutions built on AWS.

Key Responsibilities

AI Platform Strategy & Roadmap

• Define the enterprise AI platform vision and target operating model.
• Develop multi-year AI platform roadmaps aligned to business and technology objectives.
• Assess current AI, data, analytics, and cloud capabilities.
• Identify platform gaps, dependencies, and modernization priorities.
• Establish phased implementation plans and investment recommendations.

AWS AI & Data Architecture Strategy

• Define target-state AI platform architecture leveraging AWS services.
• Develop architecture patterns for:
  • Generative AI
  • Agentic AI
  • Machine Learning
  • Data Science Workbenches
  • Model Lifecycle Management
  • AI Application Development
• Establish design principles for scalability, interoperability, security, and reusability.
• Guide integration of AI capabilities with enterprise applications and business workflows.

AI Services & Platform Capability Development

Lead strategy for platform capabilities including:

• Amazon Bedrock
• Amazon SageMaker
• AWS Glue
• Amazon EMR
• Amazon Redshift
• Amazon OpenSearch
• Amazon Kendra
• Amazon EKS
• AWS Lambda
• AWS Step Functions

Define platform services such as:

• Prompt Management
• Model Catalogs
• Feature Stores
• Vector Databases
• RAG Frameworks
• Agent Orchestration
• MLOps
• LLMOps
• API Management
• AI Monitoring and Observability

AI Governance & Responsible AI

• Define AI governance frameworks and operational controls.
• Establish model governance and lifecycle management processes.
• Create policies for:
  • Responsible AI
  • Data Privacy
  • Security
  • Compliance
  • Risk Management
• Define AI guardrails and human-in-the-loop review processes.
• Align platform capabilities with industry and regulatory requirements.

Demand Management & Use Case Prioritization

• Design frameworks for intake, assessment, and prioritization of AI opportunities.
• Create value-based scoring methodologies.
• Align business demand with platform capabilities and implementation capacity.
• Develop repeatable processes for onboarding new AI use cases.
• Maintain enterprise AI opportunity portfolios and roadmaps.

Operating Model & Organizational Readiness

• Define AI platform ownership and organizational structures.
• Establish AI Centers of Excellence (CoE).
• Develop platform support and service models.
• Recommend talent, skills, and capability‑building strategies.
• Drive adoption and change management initiatives.

Executive Advisory

• Advise executive stakeholders on AI platform investments and priorities.
• Present AI strategy, architecture, and roadmap recommendations.
• Develop board‑level and executive business cases.
• Align platform initiatives to measurable business outcomes and ROI.

Required Qualifications

• 10+ years of experience in enterprise architecture, cloud strategy, data strategy, AI strategy, or digital transformation.
• 5+ years working with AWS cloud technologies.
• Experience designing enterprise AI or data platforms.
• Strong understanding of:
  • Generative AI
  • Agentic AI
  • Machine Learning
  • Data Platforms
  • MLOps and LLMOps
  • Cloud‑Native Architectures
• Experience developing executive‑level roadmaps and business cases.
• Strong communication and stakeholder management skills.

Preferred Qualifications

AWS Certifications

• AWS Certified Solutions Architect Professional
• AWS Certified Machine Learning Specialty
• AWS Certified Data Engineer
• AWS Certified AI Practitioner

Industry Experience

• Healthcare
• Payer
• Provider
• Life Sciences
• Highly regulated industries

Additional Experience

• Databricks
• Snowflake
• Collibra
• Informatica
• Data Governance Platforms
• AI Governance Platforms

Key Deliverables

• Enterprise AI Platform Strategy
• AWS AI Reference Architecture
• AI Capability Assessment
• AI Maturity Assessment
• Platform Roadmap (12-36 Months)
• AI Governance Framework
• Responsible AI Framework
• AI Operating Model
• Use Case Prioritization Framework
• AI Service Catalog
• MLOps/LLMOps Strategy

Business Value Realization Framework

We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role.

Salary and Other Compensation:

Applications will be accepted until Nov 15, 2026.

The annual salary for this position is between $80,000 - $180,000 depending on experience and other qualifications of the successful candidate.

This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.

Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:

• Medical/Dental/Vision/Life Insurance
• Paid holidays plus Paid Time Off
• 401(k) plan and contributions
• Long-term/Short-term Disability
• Paid Parental Leave
• Employee Stock Purchase Plan

Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.