ML/AI Engineer

Role Overview

As an ML/AI Engineer, you will build and scale production-grade AI features for a logistics/supply chain platform. The role focuses on shipping intelligent capabilities end-to-end, from experimentation and model development through deployment, monitoring, and iteration.

Responsibilities

• Design, build, and deploy AI-powered features across core operational workflows.
• Develop and maintain systems using large language models, including:
  • Agents
  • Retrieval pipelines (RAG)
  • Workflow automation tools
• Build ML models for use cases such as:
  • Classification
  • Forecasting
  • Anomaly detection
  • Recommendations
• Own the full lifecycle of ML systems:
  • data pipelines, experimentation, training, evaluation
  • deployment and ongoing production monitoring
• Collaborate with product, operations, and customer teams to identify and deliver high-impact AI opportunities.
• Improve scalability, reliability, and performance of AI infrastructure and inference.
• Establish best practices for prompt engineering, model evaluation, observability, and cost optimization.

Requirements

• 4+ years of software engineering experience with significant exposure to ML/applied AI.
• Strong backend engineering fundamentals and experience building scalable, production-ready systems.
• Hands-on experience shipping ML/LLM-powered products into production.
• Proficiency in Python and modern ML frameworks: PyTorch, TensorFlow, and/or scikit-learn.
• Desirable: experience with LLM APIs and tooling such as OpenAI, Anthropic, LangChain, or LlamaIndex.
• Experience with vector databases, retrieval systems, and AI orchestration frameworks.
• Solid understanding of API design, distributed systems, and cloud infrastructure.
• Strong ownership mindset and ability to translate ambiguous business problems into technical solutions.

Nice to Have

• Experience with operational workflows and document intelligence.
• Experience with fine-tuning models.
• Experience working in high-growth startup environments.