AIML

Key Responsibilities:
Fine-tune and optimize LLMs such as LLaMA and OpenAI GPT using advanced prompt
engineering and parameter-efficient techniques (LoRA quantization).
Design and implement end-to-end RAG pipelines with vector search (FAISS hybrid
retrieval re-ranking).
Build autonomous AI agents using LangChain and modern Agent Development Kits
(ADK) including tool-calling memory management and multi-agent orchestration.
Develop and optimize ML/DL models using PyTorch and TensorFlow including
multimodal architectures.
Build scalable APIs using FastAPI/Flask and deploy AI systems on AWS/Azure.
Implement guardrails evaluation metrics monitoring and performance optimization for
production AI systems.
Containerize and manage deployments using Docker and Git.
Required Qualifications:
Strong proficiency in Python with solid understanding of Data Structures & Algorithms.
Hands-on experience with PyTorch TensorFlow and Hugging Face Transformers.
Experience building and fine-tuning LLMs (e.g. LLaMA OpenAI GPT) including LoRA
and quantization techniques.
Strong experience in designing RAG pipelines and implementing vector search (FAISS
hybrid retrieval).
Experience building AI agents with tool-calling memory management and orchestration
(LangChain/ADK)
Experience developing APIs using FastAPI or Flask.
Working knowledge of SQL/MySQL Redis and cloud deployment (AWS/Azure).
Familiarity with Docker Git and production deployment practices

Tasks

Fine-tune and optimize LLMs such as LLaMA and OpenAI GPT using advanced prompt
engineering and parameter-efficient techniques (LoRA quantization).
Design and implement end-to-end RAG pipelines with vector search (FAISS hybrid
retrieval re-ranking).
Build autonomous AI agents using LangChain and modern Agent Development Kits
(ADK) including tool-calling memory management and multi-agent orchestration.
Develop and optimize ML/DL models using PyTorch and TensorFlow including
multimodal architectures.
Build scalable APIs using FastAPI/Flask and deploy AI systems on AWS/Azure.
Implement guardrails evaluation metrics monitoring and performance optimization for
production AI systems.
Containerize and manage deployments using Docker and Git

Requirements

Strong proficiency in Python with solid understanding of Data Structures & Algorithms.
Hands-on experience with PyTorch TensorFlow and Hugging Face Transformers.
Experience building and fine-tuning LLMs (e.g. LLaMA OpenAI GPT) including LoRA
and quantization techniques.
Strong experience in designing RAG pipelines and implementing vector search (FAISS
hybrid retrieval).
Experience building AI agents with tool-calling memory management and orchestration
(LangChain/ADK)
Experience developing APIs using FastAPI or Flask.
Working knowledge of SQL/MySQL Redis and cloud deployment (AWS/Azure).
Familiarity with Docker Git and production deployment practices.