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ML / Fine-Tuning Engineer
DATAECONOMY
Hyderabad, IND
Full-time
Senior · 5+ years experience
Hybrid
Discovered 1 weeks ago
QwenLlamaSFTGRPODPOLoRA
Free
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QwenLlamaSFT
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QwenLlamaSFTGRPODPOLoRA
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What We Expect
Fine-tune open-source LLMs (Qwen, Llama) from experiment to production-ready checkpoint
Run SFT and RL alignment (GRPO, DPO) to improve output accuracy
Execute training on AWS GPU instances (p4d, p5, g5) using distributed training
Diagnose/fix training issues: loss imbalances, OOM errors, gradient instabilities
Collaborate with evaluation and data engineering to iterate on quality gaps
Make data-driven model scaling decisions (8B → 14B → 70B) based on offline metrics
Requirements
- Experience: 5+ years ML engineering, with 2+ years in LLM fine-tuning
- LLM Models: Hands-on with open-source LLMs — Qwen and Llama required
- Training Methods: SFT, LoRA/QLoRA, GRPO, DPO/RLHF
- Frameworks: NVIDIA NeMo/NeMoRL, VeRL, HuggingFace TRL — must have used at least two
- Distributed Training: DeepSpeed ZeRO, FSDP2, multi-node GPU orchestration
- AWS Infrastructure: p4d/p5/g5 GPU instances, SageMaker Training Jobs
- Languages: Python, PyTorch; CUDA debugging a plus
- Preferred (Not Required): Fine-tuning for tool-calling/agent tasks; multi-node GRPO/RLHF with NeMoRL or VeRL; tokenizer internals and chat-template rendering for tool-use formats.
Benefits
- Comprehensive Medical Coverage: Health insurance of INR 5.0 Lakhs for you and your family (up to 6 members), ensuring complete peace of mind.
- Robust Protection Plans: Group Personal Accident Insurance and Group Term Life Insurance to safeguard you and your loved ones.
- Retirement Benefits: PF and Gratuity provided as per standard government regulations.
- Flexible Work Options: Enjoy hybrid work arrangements & flexible working hours.
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