Our Client Currently looking for On Device Machine Learning Engineer – LLM Fine Tuning

Key Responsibilities :

– Fine-tune and optimize small language models for domain-specific AI applications

– Build and manage multiple LoRA adapters for different AI health agents

– Implement dynamic adapter switching based on user context and application workflows

– Perform model quantization using INT4/INT8, GGUF, GPTQ, and related techniques

– Deploy optimized models on-device using frameworks such as CoreML, TFLite, llama.cpp, or MLC-LLM

– Curate training datasets and prepare instruction-tuning datasets for model enhancement

– Optimize model performance for low-latency and resource-efficient edge deployments

– Collaborate with product, AI, and mobile engineering teams to integrate AI capabilities into applications

– Evaluate model performance, inference efficiency, and deployment scalability

– Support experimentation and innovation around LLM optimization and alignment techniques

Required Skills & Experience :

– Strong hands-on experience with LoRA and QLoRA fine-tuning techniques

– Experience fine-tuning small LLMs (0.5B3B parameter range) such as :

i. Phi-3

ii. Gemma 2

iii. Llama 3.2

– Strong understanding of model quantization techniques including :

i. INT4 / INT8

ii. GGUF

iii. GPTQ

– Experience with on-device deployment using :

i. CoreML

ii. TFLite

iii. llama.cpp

iv. MLC-LLM

– Experience in training data curation and instruction dataset preparation

– Expertise in Multi-LoRA adapter management and dynamic adapter switching

– Strong Python and ML engineering skills

– Excellent analytical and problem-solving abilities

Nice to Have :

– Experience fine-tuning models on health, biomedical, or wellness datasets

– Familiarity with RLHF or DPO alignment techniques

– Android or iOS ML integration experience

– Exposure to edge AI optimization and mobile inference acceleration

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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