Gen AI Developer

Role Overview

We are seeking a skilled Gen AI Developer to design, develop, and deploy scalable, production-grade generative AI solutions. This role requires strong proficiency in Python, expertise in Generative AI techniques, and hands-on experience with cloud infrastructure, particularly AWS.

Responsibilities

• 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.

Requirements

• 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 open source LLMs based solution design.
• Technical Proficiency: Overall understanding of Machine learning algorithms (e.g., linear regression, neural networks).
• Familiarity with data science tools: NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Keras.
• Exposure to cloud computing platforms: AWS, Azure, GCP.
• Familiarity with NLP (Transformer models, attention mechanisms, word embeddings) and Computer Vision (CNNs, RNNs).
• Familiarity with Python parallel processing modules such as multiprocessing, concurrent.futures, dask.
• 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, pytest, SonarQube, SonarLint, and Black Duck.
• Solid understanding of security best practices in cloud and AI deployments.