Lead Machine Learning Engineer

Summary

Folio3 is looking for a Lead Machine Learning Engineer to drive innovation across AI initiatives from conceptualization to scalable deployment. The ideal candidate combines deep technical expertise with strong leadership, guiding a high-performing team in developing advanced ML and Agentic AI solutions that power next-generation enterprise applications.

Responsibilities

• Lead the design, development, and deployment of end-to-end AI/ML systems, including model training, inference, and performance optimization.
• Research and implement Agentic AI architectures, enabling autonomous decision-making and task orchestration across enterprise applications.
• Evaluate, compare, and optimize Deep Learning models for Computer Vision, NLP, and Predictive Analytics use cases.
• Architect and deploy scalable AI solutions on AWS, Azure, or GCP using MLOps best practices.
• Build and maintain backend APIs for model inference and data services (Python, FastAPI, Flask).
• Establish best practices for model versioning, experiment tracking, and monitoring using tools like ClearML, MLflow, or Weights & Biases.
• Collaborate cross-functionally with data engineers, product managers, and business stakeholders to translate strategic goals into technical deliverables.
• Mentor junior engineers and guide the ML team on design, research, and implementation strategies.
• Stay ahead of emerging trends in Generative AI, LLMs, and Agentic AI systems and integrate them into Folio3's AI roadmap.

Requirements

• 7+ years of professional experience in Machine Learning, Deep Learning, or Applied AI.
• Proven experience in designing and deploying production-grade ML systems and AI-driven APIs.
• Strong understanding of CNNs, RNNs, Transformers, and Reinforcement Learning techniques.
• Expertise in EDA, Feature Engineering, and model interpretability.
• Hands-on experience with PyTorch, TensorFlow, scikit-learn, XGBoost, PyCaret, or similar frameworks.
• Proficiency in Python, FastAPI/Flask, and Linux-based environments.
• Experience implementing MLOps pipelines for model lifecycle automation.
• Knowledge of Agentic AI frameworks and multi-agent orchestration (LangChain, CrewAI, AutoGen, or similar).
• Excellent leadership, communication, and problem-solving skills with the ability to mentor teams and manage complex projects.
• Passionate about AI innovation, with a continuous learning mindset and strong research orientation.