Familiarity with Python parallel processing modules such as multiprocessing, concurrent.futures, dask for efficient parallel and distributed computing.
Hands-on experience with GenAI frameworks such as LangChain, LangGraph, and Prompt Engineering.
Proficient in AWS cloud services and cloud-native architecture.
Skilled in Infrastructure as Code (IaC) using Terraform.
Familiar with CI/CD pipelines, Docker, and Kubernetes.
Familiarity with code quality tools such as pylint, black, isort, mypy, pytest, SonarQube, SonarLint, and Black Duck for linting, formatting, testing, static analysis, and open-source security compliance
Solid understanding of security best practices in cloud and AI deployments.
Strong Python proficiency with experience in FastAPI, asyncio, modular application design, and parallel processing.
Develop scalable and modular Python applications for deploying generative AI solutions.
Build and manage cloud infrastructure using AWS services (S3, Lambda, DynamoDB, ECS, EKS).
Automate infrastructure provisioning and configuration using Terraform.
Collaborate with data scientists, ML engineers, and product teams to integrate AI models into domain-specific applications.
Ensure production-grade scalability, reliability, and security of GenAI systems.
Monitor and optimize system performance using tools like AWS CloudWatch.
Stay updated with advancements in GenAI, cloud computing, MLOps, and DevOps.
Contribute to code reviews, documentation, and Python development best practices.
