2.0 - 10.0 years

0 Lacs

coimbatore, tamil nadu

On-site

You should have 3 to 10 years of experience in AI development and be located in Coimbatore. Immediate joiners are preferred. A minimum of 2 years of experience in core Gen AI is required. As an AI Developer, your responsibilities will include designing, developing, and fine-tuning Large Language Models (LLMs) for various in-house applications. You will implement and optimize Retrieval-Augmented Generation (RAG) techniques to enhance AI response quality. Additionally, you will develop and deploy Agentic AI systems capable of autonomous decision-making and task execution. Building and managing data pipelines for processing, transforming, and feeding structured/unstructured data into AI models will be part of your role. It is essential to ensure scalability, performance, and security of AI-driven solutions in production environments. Collaboration with cross-functional teams, including data engineers, software developers, and product managers, is expected. You will conduct experiments and evaluations to improve AI system accuracy and efficiency while staying updated with the latest advancements in AI/ML research, open-source models, and industry best practices. You should have strong experience in LLM fine-tuning using frameworks like Hugging Face, DeepSpeed, or LoRA/PEFT. Hands-on experience with RAG architectures, including vector databases such as Pinecone, ChromaDB, Weaviate, OpenSearch, and FAISS, is required. Experience in building AI agents using LangChain, LangGraph, CrewAI, AutoGPT, or similar frameworks is preferred. Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow is necessary. Experience in Python web frameworks such as FastAPI, Django, or Flask is expected. You should also have 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) is essential. Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, Cohere, Llama, etc.) and their integration in applications is a plus. A strong understanding of vector search, embedding models, and hybrid retrieval techniques is required. Experience with optimizing inference and serving AI models in real-time production systems is beneficial. Experience with multi-modal AI (text, image, audio) and familiarity with privacy-preserving AI techniques and responsible AI frameworks are desirable. Understanding of MLOps best practices, including model versioning, monitoring, and deployment automation, is a plus. Skills required for this role include PyTorch, RAG architectures, OpenSearch, Weaviate, Docker, LLM fine-tuning, ChromaDB, Apache Airflow, LoRA, Python, hybrid retrieval techniques, Django, GCP, CrewAI, OpenAI, Hugging Face, Gen AI, Pinecone, FAISS, AWS, AutoGPT, embedding models, Flask, FastAPI, LLM APIs, DeepSpeed, vector search, PEFT, LangChain, Azure, Spark, Kubernetes, AI Gen, TensorFlow, real-time production systems, LangGraph, and Kafka.,

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