Computer Scientist ( Python )

As a Senior Software Developer with proficiency in Python and Generative AI, you will be act as a technical leader responsible for architecting and deploying production-grade AI systems. Beyond standard coding, this role bridges the gap between AI research and scalable software engineering, focusing on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic workflows

Core Responsibilities

· System Architecture & Build: Lead the build of scalable backend systems and robust architectures for platforms powered by artificial intelligence, ensuring high availability and security.

· GenAI Implementation: Build and deploy production-scale Agentic AI workflows and multi-model RAG pipelines using frameworks like LangChain or LlamaIndex.

· Model Optimization: Fine-tune LLMs using techniques like LoRA or QLoRA and perform prompt engineering to improve model accuracy and efficiency.

· API Development: Develop high-performance RESTful or GraphQL APIs (typically using FastAPI or Flask) to integrate AI models with enterprise applications.

· Data & MLOps: Implement data ingestion and preprocessing mechanisms, while overseeing LLMOps practices such as model versioning, monitoring, and CI/CD for AI services.

· Leadership & Mentorship: Provide technical guidance to junior developers, conduct rigorous code reviews, and foster a collaborative environment adhering to standard methodologies

Technical Skills & Qualifications

· Advanced Python: Expert-level proficiency (typically 5-8+ years) in Python, including asynchronous programming (asyncio), OOP, and design patterns.

· AI Frameworks: Deep experience with Hugging Face, PyTorch, or TensorFlow, and specialized GenAI libraries like LangGraph or CrewAI.

· Vector Databases: Hands-on knowledge of vector search and storage solutions like Pinecone, Weaviate, or FAISS.

· Cloud Platforms: Strong experience deploying to cloud environments like AWS, Azure, or GCP (

· Data Engineering: Mastery of data processing libraries such as Pandas and NumPy, and experience with SQL/NoSQL databases.

· DevOps: Proficiency in Docker, Kubernetes, and CI/CD tools (e.g., Jenkins, GitHub Actions) to maintain production stability