Gen AI Developer

Experience:

Location

Notice Period:

Note: Minimum 2 years experience into core Gen AI

• Design, develop, and fine-tune Large Language Models (LLMs) for various in-house applications.
• Implement and optimize Retrieval-Augmented Generation (RAG) techniques to enhance AI response quality.
• Develop and deploy Agentic AI systems capable of autonomous decision-making and task execution.
• Build and manage data pipelines for processing, transforming, and feeding structured/unstructured data into AI models.
• Ensure scalability, performance, and security of AI-driven solutions in production environments.
• Collaborate with cross-functional teams, including data engineers, software developers, and product managers.
• Conduct experiments and evaluations to improve AI system accuracy and efficiency.
• Stay updated with the latest advancements in AI/ML research, open-source models, and industry best practices.
  

𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 & 𝗤𝘂𝗮𝗹𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀

• Strong experience in LLM fine-tuning using frameworks like Hugging Face, DeepSpeed, or LoRA/PEFT.
• Hands-on experience with RAG architectures, including vector databases (e.g., Pinecone, ChromaDB, Weaviate, OpenSearch, FAISS).
• Experience in building AI agents using LangChain, LangGraph, CrewAI, AutoGPT, or similar frameworks.
• Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow.
• Experience in Python web frameworks such as FastAPI, Django, or Flask.
• Experience in designing and managing data pipelines using tools like Apache Airflow, Kafka, or Spark.
• Knowledge of cloud platforms (AWS/GCP/Azure) and containerization technologies (Docker, Kubernetes).
• Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, Cohere, Llama, etc.) and their integration in applications.
• Strong understanding of vector search, embedding models, and hybrid retrieval techniques.
• Experience with optimizing inference and serving AI models in real-time production systems.
  

𝗡𝗶𝗰𝗲-𝘁𝗼-𝗛𝗮𝘃𝗲 𝗦𝗸𝗶𝗹𝗹𝘀

• Experience with multi-modal AI (text, image, audio).
• Familiarity with privacy-preserving AI techniques and responsible AI frameworks.
• Understanding of MLOps best practices, including model versioning, monitoring, and deployment automation.