Full Stack AI/ML Engineer

Machine Learning (ML) Full Stack Engineer: AI Agent System

About the Role:

We are seeking a talented Machine Learning Engineer to help design, train, and deploy key ML components of our AI Agentic system, which automates crew selection for live events. Youll work on two core areas:

• Fine-tuning LLMs for natural-language event creation.
• Building ranking models for human resources selection based on performance, availability, and feedback.

Youll collaborate with our Data Analyst, AI Engineers, and Backend team to deliver robust, explainable, and production-ready ML systems.

Key Responsibilities:

Model Development

• Fine-tune open-source LLMs (e.g., LLaMA 2, DeepSeek, Mistral) using domain-specific data (event records, job descriptions).
• Build a Ranker model that scores and ranks crew members using:
• Historical performance
• Event attendance
• Ratings, notes, and feedback
• Availability data
• Cancellation rates (negatively weighted)

Data Work

• Collaborate with Data Analysts to clean, preprocess, and label datasets for ML training.
• Develop robust feature engineering pipelines and transformation logic.

Evaluation & Tuning

• Experiment with different ML algorithms (XGBoost, Random Forest, or Neural Nets) for ranking.
• Evaluate model performance using precision, recall, accuracy, and business-centric metrics (quality of match).
• Run A/B testing or simulated matching if applicable.

Deployment Support

• Export trained models in a form consumable by AI Agents (ONNX, pickle, TorchScript, etc.).
• Work with Backend/DevOps teams to ensure model inference is performant and scalable.

Documentation

• Create technical documentation for model logic, assumptions, and explainability.
• Provide guidelines for continuous improvement based on feedback loops.

Required Skills & Qualifications:

• 3+ years of experience in machine learning / AI development
• Strong understanding of:
• Supervised learning (ranking, classification)
• NLP techniques (tokenization, embedding models, prompt engineering)
• Fine-tuning LLMs using LoRA or PEFT frameworks
• Hands-on with ML libraries: PyTorch, Transformers (Hugging Face), scikit-learn, XGBoost
• Strong coding skills in Python
• Experience building, evaluating, and tuning real-world ML models

Preferred candidate profile

• Experience working with event scheduling, HR tech, or workforce automation
• Familiarity with RAG systems (Retrieval Augmented Generation)
• Knowledge of MLOps or deployment workflows
• Use of GPU-enabled training environments, especially in Azure or Colab Pro, OpenAI

Tools You May Use:

• Hugging Face Transformers, Datasets
• PyTorch / TensorFlow
• Scikit-learn, XGBoost
• Azure ML / Google Colab / Jupyter
• MLflow (optional for tracking)
• Pandas, NumPy
• GitHub / Bitbucket for version control

  Role & responsibilities