AI / GenAI Engineer

Description and Requirements Role Summary We are looking for an AI/ML Engineer with strong Python skills and a passion for building production-ready AI systems. In this role, you will contribute to developing and deploying LLM-based applications, including multi-agent systems and RAG pipelines, while ensuring scalability, reliability, and cost efficiency. Key Responsibilities Model Integration: Assist in deploying and maintaining machine learning models into production environments using Python and cloud services. Agentic Frameworks :Hands-on experience with LangGraph, CrewAI, or AutoGento build multi-agent systems that can reason, plan, and execute multi-step tasks. RAG :Understanding of advanced Retrieval-Augmented Generation (RAG)techniques, including hybrid search, reranking, and semantic chunking using vector databases like Pinecone, Weaviate, or Chroma. Model Context Protocol (MCP) :Familiarity with implementing MCP serversto help LLMs interact securely with external tools and data sources. Data Pipeline Support: Help clean, preprocess, and augment datasets to improve model training and evaluation. Prompt Engineering: Design, test, and iterate on system prompts to ensure high-quality and safe AI outputs. Token Optimization: Ability to manage context window limits and optimize token usage to balance model performance with operational costs. MLOps Basics: Experience with CI/CD pipelines tailored for AI, including model versioning and automated testing of prompts within the development lifecycle. Collaboration: Participate in code reviews, sprint planning, and technical documentation to ensure code quality and knowledge sharing. Continuous Learning: Stay up-to-date with the latest AI research, libraries, and tools to suggest improvements to our tech stack. Qualifications: Education: Bachelor's degree in Computer Science, Data Science, or a related technical field (or equivalent practical experience). Programming: Proficiency in Python AI Foundations: Basic understanding of machine learning concepts (Supervised vs. Unsupervised learning) Web Basics: Familiarity with RESTful APIs and how to integrate them into a backend. Version Control: Solid grasp of Git and collaborative workflows. Problem Solving: A hacker mindset-willing to experiment, fail fast, and iterate on complex problems. Experience : 5+ years of relevant experience