Data Platform Architect

Data Platform Architect

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 : Machine Learning Operations
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

As a hands-on Engineer in AI Infrastructure Architecture, you will design, build, automate, monitor and optimize AI/ML infrastructure on AWS for reliable, scalable and cost-effective model development and production workloads. You will work on moderately complex infrastructure components under guidance from senior architects and engineers, contributing to GPU/accelerated compute environments, model deployment pipelines, observability, security and operational reliability for AI-driven business solutions.

Key Responsibilities
Write, review and debug code, scripts and infrastructure-as-code for AWS AI infrastructure, automation, monitoring and deployment tooling.
Configure and provision AWS compute resources for AI/ML workloads, including GPU-enabled instances, Amazon EC2, Amazon EKS, Amazon SageMaker and supporting storage/networking services.
Support deployment automation and CI/CD pipelines for AI systems, models and applications using tools such as Git, Terraform/CloudFormation, Docker, Kubernetes and workflow orchestration tooling.
Deploy and operate AI services, model-serving components and data pipelines while applying reliability, security, cost-efficiency and scalability practices.
Monitor infrastructure and model-serving health using AWS CloudWatch and related observability tools troubleshoot issues across compute, storage, networking, containers and application layers.
Collaborate with data scientists, ML engineers, platform engineers and architects to integrate AI models into enterprise systems while meeting compliance and operational requirements.
Document reusable patterns, configuration standards and runbooks for AWS-based AI infrastructure.

Required Qualifications
Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field.
Minimum 2 years of experience coding, building, monitoring or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines or cloud/platform engineering solutions.
Strong understanding of AI/ML concepts and the compute, storage, networking, security and deployment foundations required to run AI workloads.
Minimum 2 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash or PowerShell.
Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration and operational monitoring tools.
Strong problem-solving ability, communication skills and collaboration mindset in a fast-paced engineering environment.

Required Skills/ Experience
Hands-on experience with AWS services relevant to AI infrastructure such as EC2, EKS, SageMaker, S3, IAM, VPC, CloudWatch and related DevOps services.
Experience designing or operating GPU/accelerated compute, distributed training setups, containerized deployments and model-serving workloads.
Working knowledge of Terraform or CloudFormation, Docker, Kubernetes, CI/CD pipelines and observability practices.
Ability to optimize infrastructure for performance, reliability, scalability, cost and security.
Understanding of MLOps patterns including experiment tracking, model registry, model deployment, monitoring and rollback approaches.

Good to Have Skills
AWS certification such as AWS Certified Solutions Architect, Developer, DevOps Engineer or Machine Learning specialty/associate level.
Exposure to industry use cases in BFSI, healthcare, retail/e-commerce, telecom, manufacturing or public sector where AI infrastructure must meet compliance, reliability and data-governance expectations.
Familiarity with large language model infrastructure, vector databases, retrieval pipelines, GPU scheduling or model optimization techniques.
Knowledge of security controls, FinOps practices, incident management and production support processes for enterprise AI platforms.