* 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.

* 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 servicesand 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 Duckfor linting, formatting, testing, static analysis, and open-source security compliance
* Solid understanding of security best practicesin cloud and AI deployments.

* Generative AI Expertise: Good understanding of various Generative AI techniques, including GANs, VAEs, and other relevant architectures. Proven experience in applying these techniques to real-world problems for tasks such as image and text generation. Conversant with Gen AI development tools like Prompt engineering, Langchain, Semantic Kernels, Function calling. Exposure to both API based and opens source LLMs based solution design.
* Technical Proficiency: An overall understanding of below technologies is required :
* Machine learning algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks
* Data science tools: NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Keras
* Cloud computing platforms: AWS, Azure, GCP
* Natural language processing (NLP): Transformer models, attention mechanisms, word embeddings
* Computer vision: Convolutional neural networks, recurrent neural networks, object detection
* Robotics: Reinforcement learning, motion planning, control systems
* Data ethics: Bias in machine learning, fairness in algorithms