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Machine Learning Engineer - Generative AI & RAG

Machine Learning Engineer - Generative AI & RAG

Masin Projects Pvt. LtdGurugram
30+ days ago
Job description

Machine Learning Engineer - GenAI, RAG & Recommendations (2+ Years)

Roles and Responsibilities :

  • Build and deploy scalable LLM-based systems using OpenAI, Claude, LLaMA, or Mistral for contract understanding and legal automation.
  • Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases (FAISS, Pinecone, Weaviate).
  • Fine-tune and evaluate foundation models for domain-specific tasks like clause extraction, dispute classification, and document QA.
  • Create recommendation models to suggest similar legal cases, past dispute patterns, or clause templates using collaborative and content-based filtering.
  • Develop inference-ready APIs and backend microservices using FastAPI / Flask, integrating them into production workflows.
  • Optimize model latency, prompt engineering, caching strategies, and accuracy using A / B testing and hallucination checks.
  • Work closely with Data Engineers and QA to convert ML prototypes into productionready pipelines.
  • Conduct continuous error analysis, evaluation metric design (F1, BLEU, Recall@K), and prompt iterations.
  • Participate in model versioning, logging, and reproducibility tracking using tools like MLflow or LangSmith.
  • Stay current with research on GenAI, prompting techniques, LLM compression, and RAG design patterns.

Qualifications :

  • Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.
  • 2+ years of experience in applied ML / NLP projects with real-world deployments.
  • Experience with LLMs like GPT, Claude, Gemini, Mistral, and techniques like fine-tuning, few-shot prompting, and context window optimization.
  • Solid knowledge of Python, PyTorch, Transformers, LangChain, and embedding models.
  • Hands-on experience integrating vector stores and building RAG pipelines.
  • Understanding of NLP techniques such as summarization, token classification, document ranking, and conversational QA.
  • Bonus :

  • Experience with Neo4j, recommendation systems, or graph embeddings.
  • (ref : hirist.tech)

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