Project Role : Data Platform Architect
Project Role Description : Architects the data platform blueprint and implements the design, encompassing the relevant data platform components. Collaborates with the Integration Architects and Data Architects to ensure cohesive integration between systems and data models.
Must have skills : AWS AI Services
Good to have skills : AI Agents & Workflow Integration
Minimum 18 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
As an Technical Architect in AI Infrastructure Architecture , you will act as a senior technical authority for AWS-based AI/ML compute infrastructure, shaping the technical vision, reference architecture, standards and implementation strategy for large-scale AI systems. You will evaluate complex choices across compute, networking, storage, orchestration, model serving, observability, security and cost optimization, while guiding senior and lead architects/Technical Architects to deliver resilient, scalable and production-ready AI infrastructure. You will bring industry experience across enterprise AI adoption, compliance, reliability, FinOps and platform modernization to help clients translate AI infrastructure trade-offs into measurable business value.
Key Responsibilities
Set the overarching AWS AI infrastructure vision, strategy and reference architecture for large-scale AI/ML systems, including compute, networking, storage, orchestration, model serving and observability.
Own complex architectural decisions across AWS services such as EC2, EKS, SageMaker, S3, FSx/EFS, VPC, IAM, CloudWatch and related DevOps/security tooling, rationalizing options against client standards and business objectives.
Architect and prototype cost-optimized GPU/accelerated compute and distributed training environments, building benchmarks, proof-of-concepts and reusable implementation patterns.
Define architecture standards, reusable infrastructure-as-code patterns, CI/CD approaches, model deployment patterns, monitoring strategy, SLAs/SLOs and cost/performance governance for production AI/ML systems.
Lead architecture assessments and design reviews validate findings through hands-on implementation, profiling, performance tuning and troubleshooting across the AI infrastructure stack.
Evaluate emerging AI infrastructure technologies, accelerators, interconnects, managed AI services and ecosystem tools, and recommend where they belong in enterprise solutions.
Provide executive and client-level technical advisory, translating cloud infrastructure trade-offs into clear, defensible recommendations connected to business outcomes.
Mentor architects and Technical Architects, build community best practices and represent the practice in internal and external technical forums.
Required Qualifications
Bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology or a related Technical Architecting field.
Minimum 6 years of experience coding, building, monitoring, troubleshooting, designing and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines or large-scale Technical Architecting solutions.
Strong understanding of AI/ML concepts and the compute, infrastructure, orchestration and deployment foundations required to run production AI systems.
Minimum 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell or equivalent Technical Architecting languages.
Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services or platform-native workflow tooling.
Proven experience leading AI infrastructure projects and teams, including technical direction, design reviews, delivery governance and stakeholder alignment.
Strong project management, communication, problem-solving and cross-functional collaboration skills in fast-paced client or enterprise environments.
Demonstrated experience evaluating and selecting AI technologies, frameworks, reference architectures and platform services for production solutions.
Required Skills/ Experience
Expert-level hands-on architecture experience with AWS AI infrastructure services including EC2, EKS, SageMaker, S3, IAM, VPC, CloudWatch and automation/DevOps services.
Deep knowledge of GPU/accelerated compute, distributed training, model serving, container platforms, storage design, network design, observability and resilience Technical Architecting.
Strong experience with Terraform/CloudFormation, CI/CD, Docker, Kubernetes, security guardrails, monitoring and infrastructure cost optimization.
Ability to evaluate multiple AWS architecture options and produce standards, patterns, decision records, benchmarks and executive-ready recommendations.
Experience applying MLOps/InfraOps practices for experiment tracking, model registry, deployment automation, monitoring, incident response and rollback strategies.
Good to Have Skills
AWS certifications such as Solutions Architect Professional, DevOps Technical Architect Professional or Machine Learning specialty/associate credentials.
Industry experience designing AI infrastructure for BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector environments with compliance, security and reliability constraints.
Exposure to LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, model optimization, high-performance storage and low-latency model serving.
Experience with enterprise architecture governance, technology roadmaps, vendor/partner management, FinOps and production support operating models.
15 years full time education
