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Open nowPosted 37 days ago

Machine Learning Engineer

Deccan AI Automation16 open roles

Where
Hyderabad
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Your applicationOpen nowMachine Learning EngineerDeccan AI Automation · Hyderabad

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  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

11.4% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 37 days ago

The posting

Machine Learning Engineer

Location: Hyderabad | Work Mode: Onsite

About Deccan AI

Deccan AI is a GenAI data company that runs the post-training and production layer for the world's leading AI labs and enterprises — from expert feedback and RL environments to managed AI workforces and evaluation infrastructure. Backed by A91 Partners, SIG (Susquehanna International Group), and Prosus Ventures, and having grown 10x last year, Deccan works with a majority of the Magnificent 7 and the top frontier labs globally. Its global network of 1 million+ domain experts powers Helix, its hybrid eval suite, and EnterpriseOS, its scaled ops automation platform. Headquartered in Mountain View, CA.

What You'll Do

  • Build and productionize LLM, RAG and Agentic AI systems.
  • Develop scalable ML/MLOps pipelines for deployment, evaluation and monitoring.
  • Work with LLMs, vector databases, embeddings, retrieval, reranking and agent frameworks.
  • Build and optimize AI services using Python and modern ML/engineering tools.
  • Collaborate with ML, research and engineering teams to take AI systems from prototype to production.

What We're Looking For

  • 2–4 years of hands-on ML/AI engineering experience.
  • Strong Python and software engineering fundamentals.
  • Experience with RAG, Agentic AI, MLOps, LLM applications or ML infrastructure.
  • Exposure to tools such as LangChain/LangGraph, vector DBs, Docker/Kubernetes, MLflow/W&B or similar.
  • Strong problem-solving mindset and ownership.

Good to have: LLM evaluation, fine-tuning (SFT/LoRA/QLoRA/DPO), vLLM or model serving experience.

Note: This role is focused on LLM/GenAI, RAG, Agentic AI and MLOps — Computer Vision / Multimodal experience is not required.

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