AI/ML Software Engineer Lead

Role Summary

The AI / Machine Learning Lead is responsible for identifying, designing, and implementing artificial intelligence and machine learning solutions that provide enterprise value and improve engineering capabilities. In this role, you will work directly with business users to understand their existing workflows and integrate AI solutions. You will also work closely with the software engineering team to integrate AI-driven features, establish best practices for ML development, and guide the organization in adopting modern AI technologies.

Mission: Build production-ready AI applications, agents, and integrations. This role focuses on engineering AI into real systems.

Key Responsibilities

AI Strategy & Technical Leadership

• Identify opportunities to leverage AI/ML across products and internal systems.
• Evaluate emerging technologies such as LLMs and NLP models.
• Provide technical leadership for AI initiatives across the company.
• Define best practices for AI architecture, model deployment, and lifecycle management.

AI/ML Solution Development

• Design and implement AI Agents, machine learning models and pipelines.
• Integrate AI Agents and ML models into existing applications and workflows
• Create predictive analytics and insights using enterprise data

AI Integration with Software Engineering

• Work closely with software engineers to embed AI capabilities into production systems.
• Design APIs and services that expose AI capabilities to applications.
• Ensure models can be deployed, monitored, and maintained in production environments.

Model Lifecycle & MLOps

• Establish best practices for: model training, model evaluation, versioning, deployment, monitoring
• Implement MLOps pipelines and infrastructure for scalable model deployment.

AI Innovation & Research

• Prototype/evaluate new AI approaches including: LLM applications, Generative AI, AI automation tools
• Run experiments and proofs-of-concept before full production implementation.
• Stay current with advancements in AI technologies and frameworks.

Cross-Team Collaboration

• Partner with business users and engineering leaders to translate business needs into AI solutions.
• Collaborate with data engineers to ensure data pipelines support ML workloads.
• Support teams integrating AI capabilities into their workflows.

Mentorship & Knowledge Sharing

• Mentor engineers on AI & machine learning.
• Provide guidance on AI best practices.
• Help raise overall AI competency across the organization.

Timelines

First 30 days - Understand, Audit and stabilize

Goal: Learn the current AI implementations and identify opportunities to improve reliability, architecture, and AI capabilities.

First 60 days - Improve and Implement

Goal: Begin improving existing systems and implement better AI development and operational practices.

First 180 days and beyond - Build and Scale AI capability

Goal: Develop production-ready AI-solutions that drive business value and help shape company's long-term AI strategy.

Strategic Production AI System initiatives.

• Outbound truck routing optimization.
• Optimized warehouse order picking.
• Intelligent purchasing forecast.
• Perishable inventory optimization.
• Dynamic pricing engine.
• AI sales intelligence.