Machine Learning Engineer

2 days ago

Gurugram, India Masin Projects Pvt. Ltd Full time

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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