ML Engineer

24 hours ago

Bengaluru, Karnataka, India Razorthink Full time ₹ 12,00,000 - ₹ 36,00,000 per year

We are looking for a ML Engineer /Senior ML Engineer with hands-on experience in

Generative AI (GenAI) to drive innovation through cutting-edge AI/ML

solutions. This role involves designing and implementing GenAI models

and pipelines that solve business problems and enhance productivity.

You will collaborate with cross-functional teams to deliver solutions

using large language models (LLMs), transformers, and other generative

techniques.

Key Responsibilities:

Develop and fine-tune Generative AI models using LLMs (e.g., GPT, LLaMA, Claude, PaLM) for tasks like summarization, content generation,question answering, semantic search, etc.

Build end-to-end GenAI-powered solutions including prompt engineering, model training/fine-tuning, evaluation, and deployment.

Use frameworks like LangChain, LlamaIndex, and vector databases (e.g.,FAISS, Pinecone, Weaviate) to build RAG (Retrieval-Augmented Generation) pipelines.

Collaborate with product, engineering, and business stakeholders to identify AI opportunities and prototype solutions.

Conduct experiments, evaluate model performance, and optimize for accuracy, latency, and relevance.

Contribute to the development of GenAI best practices and reusable assets within the organization.

Stay up to date with the latest research and developments in the GenAI and LLM space.

Required Skills and Experience:

2+ years of experience in ML/AI roles, with 2+ focus on GenAI or LLMs.

Strong Python skills and experience with transformers, Hugging Face, PyTorch, or TensorFlow.

Deep understanding of NLP, language modeling, and generative techniques.

Experience with prompt engineering, fine-tuning or few-shot learning.

Hands-on experience with LangChain, LlamaIndex, vllm or similar GenAI orchestration frameworks.

Working knowledge of vector stores, embedding models, and retrieval techniques.

Experience with model deployment in cloud or hybrid environments (e.g., Azure, AWS, GCP).

Familiarity with MLOps tools and best practices is a plus.

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