4 - 7 years

12 - 17 Lacs

Posted:1 day ago| Platform: Naukri logo

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Job Summary

Key Responsibilities

LLMs, SLMs, and VLMs

Implement full training lifecycle: dataset prep, tokenization, optimizer tuning, evaluation, and deployment.

Work on custom LLM architectures, LoRA, QLoRA, and PEFT methods.

2. GenAI Development (25%)

embeddings, transformers, vector databases (FAISS, Pinecone, Milvus)

prompt tuning, prompt chaining, agent-based workflows

Work with Python (FastAPI), Scikit-learn, Pandas, PyTorch/TensorFlow.

3. Cloud & MLOps (25%)

AWS AI/ML stack

Implement CI/CD, model monitoring, LLM evaluation, and quality guardrails.

Integrate data engineering & MLOps workflows for scalable model deployment.

Must-Have Skills

LLM/SLM/VLM fine-tuning & training

Hugging Face, PyTorch, TensorFlow

embeddings, transformers, vector DBs

Python

prompt tuning, prompt chaining, LLM agents

LLM evaluation

AWS ML services

Logical thinking, problem-solving, and real-time project experience.

Nice to Have

Experience with RAG frameworks (LangChain, LlamaIndex).

Knowledge of cloud architectures (AWS/GCP/Azure).

Experience with vector search optimization.

Understanding of distributed training techniques.

Preferred candidate profile

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