AI / GenAI Engineer

• 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 withCI/CD pipelinestailored 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 inPython
• 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