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YTPS HR Services

AI/ML Engineer

MH, INOn-siteIndividual contributorvia jobspy_indeed
raglangchainlanggraphocrllmvector databasesllamaindexhaystack

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**Role Overview**

We are looking for a skilled and versatile AI/ML Engineer with hands\-on expertise across Retrieval\-Augmented Generation (RAG), LangChain, LangGraph, and Optical Character Recognition (OCR). The ideal candidate will design, build, and deploy intelligent AI\-powered systems that combine large language models (LLMs), document intelligence, and agentic workflows to solve real\-world business problems at scale.

**Key Responsibilities**

● Design and implement end\-to\-end RAG pipelines integrating vector databases (FAISS, Pinecone, Weaviate, ChromaDB) with LLMs for accurate, grounded responses.

● Build and optimize document ingestion pipelines including chunking strategies, embedding generation, and metadata filtering.

● Develop production\-ready LLM applications using LangChain's chains, agents, tools, and memory components.

● Implement conversational agents with memory management (buffer, summary, vector store memory) and multi\-step reasoning.

● Architect stateful multi\-agent systems using LangGraph including cyclic reasoning loops, conditional edges, and human\-in\-the\-loop workflows.

● Design and deploy OCR pipelines for processing scanned documents, PDFs, invoices, and forms using engines like Tesseract, PaddleOCR, and EasyOCR.

● Implement document layout analysis to extract tables, headers, key\-value pairs, and structured fields from unstructured documents.

● Integrate OCR outputs with downstream NLP and LLM pipelines for intelligent document processing (IDP).

● Monitor agent performance, debug hallucination issues, and implement guardrails for production deployments.

● Collaborate with product and data engineering teams to translate business requirements into scalable AI solutions.

● Document technical designs, architecture decisions, and system performance benchmarks.

**Required Skills \& Qualifications**

● Strong hands\-on experience with RAG frameworks (LlamaIndex, LangChain, Haystack) and vector databases.

● Proficiency with LangChain v0\.2\+ including LCEL, agents, tools, and structured output generation.

● Experience building stateful agent graphs with LangGraph including nodes, edges, and state schemas.

● Hands\-on experience with OCR engines (Tesseract, PaddleOCR, EasyOCR) and cloud document AI APIs (Google Vision, AWS Textract, Azure Form Recognizer).

● Proficiency in Python with experience in async programming, REST APIs, and microservices.

● Familiarity with LLM providers such as OpenAI, Anthropic Claude, Gemini, Cohere, or Mistral.

● Experience with embedding models (OpenAI Ada, BGE, SentenceTransformers) and similarity search.

● Knowledge of image pre\-processing and computer vision libraries (OpenCV, Pillow, scikit\-image).

● Familiarity with cloud platforms (AWS, GCP, Azure) for model hosting, storage, and deployment.

● Understanding of prompt engineering, output parsers, and LLM evaluation techniques.

**Preferred / Good to Have**

● Experience with multi\-modal RAG (text \+ image) and hybrid search (BM25 \+ dense retrieval).

● Knowledge of agentic patterns such as ReAct, Plan\-and\-Execute, Reflexion, and supervisor agents.

● Familiarity with LangSmith for tracing, debugging, and evaluating LLM applications.

● Experience with document AI models such as LayoutLM, Donut, or TrOCR.

● Exposure to Intelligent Document Processing (IDP) platforms and workflow automation tools.

● Knowledge of multi\-lingual OCR and Indic script recognition.

● Contributions to open\-source AI/ML or NLP projects.

**Educational Qualification**

● B.E. / B.Tech / M.Tech in Computer Science, Information Technology, or a related field.

● Equivalent practical experience with a strong portfolio of AI/ML projects will also be considered.

Pay: ₹1,000,000\.00 \- ₹1,500,000\.00 per year

Benefits:

* Cell phone reimbursement * Flexible schedule * Food provided * Health insurance * Internet reimbursement * Leave encashment * Life insurance * Paid sick time * Provident Fund

Work Location: In person

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