Acunor Infotech
Job Title:
Gen AI Fine-Tuning Engineer Location:
Dallas, TX (Hybrid 3 days onsite) Engagement Type:
Contract Overview
We are seeking a hands-on Gen AI Fine-Tuning Engineer to develop and optimize open-source large and small language models (LLMs/SLMs) for enterprise-grade digital agent workflows. The ideal candidate combines strong applied ML engineering skills with practical experience in model fine-tuning, evaluation, and deployment within Google Cloud Platform environments. Key Responsibilities
Fine-tune and adapt open-source SLMs (e.g., Gemma, LLaMA, Mistral) using curated enterprise datasets. Apply techniques such as Supervised Fine-Tuning (SFT), LoRA, and other Parameter-Efficient Fine-Tuning (PEFT) methods to optimize model performance. Operate within Google Cloud Platform using Vertex AI, BigQuery, and Cloud Storage for training, evaluation, and orchestration. Collaborate with data engineering and data science teams to ensure datasets are well-labeled, balanced, and structured for training. Conduct benchmarking and optimization to improve model accuracy, latency, and cost-efficiency. Package, validate, and integrate fine-tuned models into digital agent and retrieval-augmented generation (RAG) workflows. Required Skills and Experience
5+ years of experience in Machine Learning or AI engineering. At least 1 year of hands-on experience in LLM/SLM fine-tuning. Strong proficiency with Hugging Face Transformers, PyTorch, and TensorFlow. Proven experience applying LoRA/PEFT and Supervised Fine-Tuning (SFT) techniques. Hands‑on experience with the Google Cloud Platform AI stack, including Vertex AI, BigQuery, Cloud Run, and Cloud Functions. Familiarity with multi‑node, multi‑GPU training environments for distributed fine‑tuning and scaling. Strong collaboration skills across data, infrastructure, and application teams to enable production‑grade deployments.
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Gen AI Fine-Tuning Engineer Location:
Dallas, TX (Hybrid 3 days onsite) Engagement Type:
Contract Overview
We are seeking a hands-on Gen AI Fine-Tuning Engineer to develop and optimize open-source large and small language models (LLMs/SLMs) for enterprise-grade digital agent workflows. The ideal candidate combines strong applied ML engineering skills with practical experience in model fine-tuning, evaluation, and deployment within Google Cloud Platform environments. Key Responsibilities
Fine-tune and adapt open-source SLMs (e.g., Gemma, LLaMA, Mistral) using curated enterprise datasets. Apply techniques such as Supervised Fine-Tuning (SFT), LoRA, and other Parameter-Efficient Fine-Tuning (PEFT) methods to optimize model performance. Operate within Google Cloud Platform using Vertex AI, BigQuery, and Cloud Storage for training, evaluation, and orchestration. Collaborate with data engineering and data science teams to ensure datasets are well-labeled, balanced, and structured for training. Conduct benchmarking and optimization to improve model accuracy, latency, and cost-efficiency. Package, validate, and integrate fine-tuned models into digital agent and retrieval-augmented generation (RAG) workflows. Required Skills and Experience
5+ years of experience in Machine Learning or AI engineering. At least 1 year of hands-on experience in LLM/SLM fine-tuning. Strong proficiency with Hugging Face Transformers, PyTorch, and TensorFlow. Proven experience applying LoRA/PEFT and Supervised Fine-Tuning (SFT) techniques. Hands‑on experience with the Google Cloud Platform AI stack, including Vertex AI, BigQuery, Cloud Run, and Cloud Functions. Familiarity with multi‑node, multi‑GPU training environments for distributed fine‑tuning and scaling. Strong collaboration skills across data, infrastructure, and application teams to enable production‑grade deployments.
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