- 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.