Generative AI Engineer / Agentic AI

• • LangGraph, LangChain, OpenAI APIs, aur Model Context Protocol (MCP) 

  • Custom routers, memory retention mechanisms, aur automated task execution agents design .

• Retrieval-Augmented Generation (RAG) Systems:

  • Hybrid search pipelines (Dense + Sparse retrieval jaise FAISS, Qdrant, ChromaDB, BM25, Reciprocal Rank Fusion - RRF) implement and optimize .

  • Unstructured data (Text, PDFs, Tax/Legal docs, Knowledge Bases)  high-precision context retrieval and Graph RAG (Neo4j) solutions build .

• LLM Evaluation & Hallucination Guardrails:

  • RAGAS, LangSmith, MLflow using frameworksfor evaluation metrics (Precision, Recall, Faithfulness, Answer Quality) track karna aur hallucinations detect .

• Model Fine-Tuning & Computer Vision / NLP:

  • Pre-trained Transformer models (BERT, RoBERTa, Vision Transformers - ViT) ko PEFT, LoRA, QLoRA techniques fine tune .

• Deployment & Integration:

  • AI services ko FastAPI / Flask / Docker through RESTful APIs and  package and deploy on Cloud Platforms (AWS SageMaker/S3/EC2, GCP) 

  • External platforms (WhatsApp Web JS, Payment Gateways, CRM Tools, Google Services) API integrations 

Required Skills & Qualifications

• Core Technical Skills:

  • Languages: Python (Advanced), SQL, Bash.

  • GenAI Frameworks: LangChain, LangGraph, LLaMA-Index, MCP, Hugging Face Transformers, LLaMA, Gemini Pro, GPT-4o.

  • Vector & Graph DBs: Qdrant, FAISS, ChromaDB, Elasticsearch, Neo4j.

  • Backend & DevOps: FastAPI, Flask, REST APIs, Docker, Git/GitHub, CI/CD.

  • Machine Learning & Deep Learning: PyTorch, TensorFlow, Scikit-Learn, Fine-tuning (LoRA, QLoRA).

  • Cloud & Big Data: AWS (SageMaker, S3, EC2), GCP (BigQuery), Apache Spark, ETL Pipelines.

• Soft Skills & Analytical Thinking:

  • Mathematics, Statistics, Linear Algebra, Calculus, aur Data Structures me strong foundational knowledge.

  • Problem-solving mindset, R&D curiosity, aur team collaboration skills.