About the Role

We are looking for a sharp, hands-on AI Engineer with 3 to 5 years of experience in Large Language Models (LLMs) and Agentic AI to work alongside a Senior AI Scientist. You will contribute to the design, fine-tuning, and deployment of AI models, enabling goal-driven, autonomous AI agents to perform complex reasoning and decision-making tasks. Your role will involve building and optimizing LLMs, integrating them with retrieval-augmented generation (RAG), and enhancing their efficiency for real-time automation.

• Key Responsibilities1. Agentic AI Development & Workflow OptimizationDevelop autonomous AI agents capable of dynamic decision-making and task execution.
• Work with LangGraph, LangChain, LlamaIndex, and other frameworks to create multi-agent workflows.
• Optimize goal-driven AI behavior for handling complex automation and reasoning tasks.
• Enhance agent collaboration mechanisms, improving their ability to break down goals, delegate tasks, and refine execution strategies.
• 2. Language Model Fine-Tuning & OptimizationWork with Tiny LLMs / Small LLMs (e.g., Phi-3, OpenELM, Mistral, Llama 3) to build efficient, scalable AI models.
• Fine-tune models using full fine-tuning, LoRA, QLoRA, and PEFT techniques to improve task-specific performance.
• Optimize GPU memory and compute resources to efficiently run fine-tuned models for real-world applications.
• Research and implement efficient inference strategies for deploying LLMs in production.
• 3. Retrieval-Augmented Generation (RAG) & Knowledge IntegrationDesign and implement RAG architectures to improve contextual awareness and decision-making capabilities of AI agents.
• Work with vector databases (e.g., ChromaDB, FAISS, Weaviate) for efficient knowledge retrieval and document understanding.
• Develop techniques for continuous learning, enabling agents to refine knowledge over time.
• 4. NLP & Intelligent Document ProcessingWork on OCR-based automation, enhancing document understanding using Tesseract, PaddleOCR, and AI-powered text extraction.
• Develop custom tokenization, embeddings, and NLP pipelines for document classification, summarization, and intent recognition.
• Implement domain-specific adaptations of LLMs to improve accuracy and performance in structured and unstructured text processing.
• 5. AI Infrastructure & Compute OptimizationConfigure fine-tuning infrastructure, manage GPU/TPU memory, and optimize compute for LLM fine-tuning.
• Experiment with different fine-tuning strategies to optimize models for low-latency, high-performance applications.
• Required Skills & QualificationsCore AI & NLP ExpertiseStrong background in NLP, LLMs, and deep learning with experience in fine-tuning and optimizing models.
• Proficiency in Python and experience with Hugging Face Transformers, LangChain, LlamaIndex, and ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Experience with Tiny LLMs / Edge AI and optimizing Phi-3, OpenELM, and Mistral for efficiency.
• RAG & Data EngineeringSolid understanding of Retrieval-Augmented Generation (RAG) and its application in agentic AI workflows.
• Experience working with SQL & NoSQL databases (PostgreSQL, MongoDB, ChromaDB, FAISS, etc.).
• Familiarity with big data processing frameworks like Spark, Dask, or Ray for handling large-scale workloads.
• AI Compute & Infrastructure (Plus, Not Mandatory)Experience deploying AI models using Docker, Kubernetes, or serverless architectures.
• Knowledge of MLOps best practices for model retraining, monitoring, and optimization.
• Why Join Us?Work on cutting-edge AI-powered agentic automation at the intersection of LLMs, RAG, and multi-agent systems.
• Be part of a team building goal-driven, autonomous AI agents that redefine how businesses leverage AI.
• Collaborate with a highly skilled Senior AI Scientist and grow your expertise in LLM fine-tuning, optimization, and AI-driven automation.
• Competitive compensation and access to state-of-the-art AI models and compute resources.