Job Title : Gen AI Engineer - Model, Fine-Tuning
Location : Dallas, TX (3 days a week Hybrid)
Engagement Type : Contract
Overview
This is a hands-on role requiring deep expertise in LLM fine-tuning, data curation, and reinforcement learning optimization , with the goal of reducing model hallucinations and enhancing contextual accuracy for production-grade cognitive systems.
Key Responsibilities
- Fine-tune large-scale LLMs (e.g., GPT, Claude, LLaMA, Mistral) using curated domain datasets for banking, risk, and compliance workflows.
- Collaborate with data engineering teams to build high-quality, labeled datasets for supervised and reinforcement learning.
- Apply advanced context engineering and prompt optimization techniques to improve model interpretability and reasoning.
- Evaluate and mitigate model drift, bias, and hallucination using quantitative performance metrics.
- Develop and automate evaluation pipelines for continuous fine-tuning and model retraining.
- Partner with the Cognitive Agent Development team to integrate tuned models into agentic workflows and decision chains.
- Contribute to model governance, versioning, and audit frameworks to ensure explainability and compliance.
Required Skills & Experience
5 10 years of hands-on experience in AI / ML, with a focus on LLM fine-tuning, prompt engineering, or context adaptation .Strong proficiency with Python, PyTorch, TensorFlow , and frameworks like Hugging Face Transformers, LangChain, and PEFT (Parameter-Efficient Fine-Tuning) .Proven experience building and labeling domain-specific datasets and applying data augmentation strategies.Familiarity with RLHF (Reinforcement Learning with Human Feedback) and evaluation metrics for generative models.Understanding of multi-agent architectures , orchestration frameworks (LangGraph, CrewAI, AutoGen, etc.), and memory management for AI agents .Exposure to banking, risk analytics, or compliance data preferred.Strong grounding in data security, privacy, and model governance standards in regulated industries.Preferred Qualifications
Master's or PhD in Computer Science, AI, or related discipline.Experience deploying LLM-based agents in production environments.Knowledge of vector databases (FAISS, Pinecone, Chroma) and retrieval-augmented generation (RAG) pipelines.Contributions to open-source AI projects or publications in fine-tuning, evaluation, or multi-agent systems.