Sudharshan V

Certified Cloud AI/ML Engineer holding AWS Certified Developer Associate with 6.5 years of experience architecting production-grade AI/ML and GenAI solutions. Proven track record in delivering scalable, reliable serverless systems and GenAI workflows using LangChain, LangGraph, and FastAPI across AWS, GCP, and Azure. Skilled in Python, TensorFlow, PyTorch, and infrastructure as code; known for driving cost optimization, cross-functional collaboration, and technical mentorship to deliver high-impact AI solutions.

Senior Software Engineer at GlobalLogic (2025-05 – Present)

• Working on AWS CDK (TypeScript) to architect and provision cloud infrastructure for large-scale applications, applying Infrastructure as Code and automation to enable DevOps practices for consistent, repeatable deployments.
• Developed and maintained serverless applications using AWS Lambda, API Gateway, RDS, DynamoDB, and Cognito, with backend logic in Node.js and Python, leveraging AWS SAM for local testing and streamlined deployments.
• Built data pipelines and storage solutions leveraging S3, CloudWatch, EventBridge, SQS, and SNS, orchestrating workflows with AWS Step Functions to ensure reliable end‑to‑end processing.
• Led cross‑functional collaboration and exercised stakeholder management with product and DevOps teams to deliver scalable, cost‑effective cloud solutions.
• Applied strategic thinking to infrastructure‑as‑code, automation, and AWS best practices, ensuring high availability and resilience while optimizing cost.
• Designed and optimized ETL jobs using AWS Glue, PySpark, and Python to transform and load large-scale datasets into S3 data lakes and Databricks (on Azure), improving data accessibility for downstream analytics.
• Created a Lambda optimization framework using Boto3—problem‑solving the cost‑per‑invocation challenge—by benchmarking memory configurations, cutting serverless function costs by 20%.
• Established internal technical mentorship programs to elevate team expertise in serverless architectures and AWS best practices, fostering a culture of continuous learning and high-quality engineering.

Software Engineer at Antstack Technologies (2020-10 – 2025-05)

• Architected scalable, fault‑tolerant cloud architecture on EC2 and microservices, performed technical design of data models, and delivered end‑to‑end ETL development on AWS using Infrastructure as Code while optimizing resource usage for cost and performance.
• Implemented IAM, encryption, and network security with automation, establishing robust data governance and disaster recovery practices that ensured compliance and data protection.
• Built CI/CD pipelines using GitHub Actions and AWS CodeBuild, applying DevOps practices for automated, reliable deployments.
• Designed and deployed serverless architectures backed by SQL/NoSQL databases (RDS & DynamoDB) using AWS Lambda, API Gateway, and AWS SAM.
• Monitored infrastructure using CloudWatch and integrated automated disaster recovery solutions, ensuring business continuity with minimal RTO.
• Led integration of AWS CloudFormation and CodePipeline, managing the full lifecycle of infrastructure deployments and reducing deployment time by 60%.
• Designed and implemented microservices architecture and backend APIs in Node.js, Python, Django, and Flask for real‑time stream processing with SQS, enabling low‑latency data handling across services.
• Engineered end-to-end GenAI solutions leveraging LangChain, LangGraph, and FastAPI to orchestrate complex AI/ML workflows, using Boto3 for AWS service integration, while fine-tuning models with TensorFlow and PyTorch to enhance predictive accuracy and system intelligence.
• Engineered a GraphQL-based API layer to streamline data retrieval for complex AI-driven applications, reducing over-fetching and improving response times for front-end services.

Bachelor's Degree in Computer Science – Maharaja Institute of Technologies (2016-08 – 2020-08)