Senior Manager, Application & Machine Learning Engineer

Description

The company's technology organization is partway through a digital transformation meant to connect its products, customers and business operations worldwide.

Its scale and complexity give rise to data, machine learning and AI problems worth solving.

This is a hands-on individual contributor role building machine learning, optimization and AI systems that inform commercial decisions across the global business, working with large enterprise datasets.

You will create forecasting models, predictive analytics, optimization engines and agentic AI applications that help business teams decide on pricing, promotions, revenue growth, profitability and operations.

You will work across classic machine learning, generative AI, retrieval-augmented generation (RAG) and agentic AI, alongside product managers, engineers, architects and business stakeholders, turning hard business questions into scalable production systems.

Strong ML fundamentals are essential, as is hands-on experience putting AI applications into production and keeping them monitored, maintained and improving.

The company has operated for 130+ years.

Responsibilities

• Build production AI and machine learning solutions that support commercial and operational decisions.
• Create predictive, recommendation, forecasting and optimization models on large structured and unstructured datasets.
• Develop generative AI applications using LLMs, prompt engineering, RAG and agentic AI frameworks.
• Build AI agents and decision-support tools that give business users grounded, explainable, actionable recommendations.
• Own solutions end to end: development, deployment, monitoring, evaluation and ongoing improvement.
• Track model and agent performance, catch drift, assess outputs and raise effectiveness over time.
• Make sure solutions meet enterprise standards for security, privacy, reliability and responsible AI.
• Write production-quality Python and reusable engineering components.
• Build APIs, microservices and distributed services for AI and ML applications.
• Set up automated testing, CI/CD, deployment automation, observability and operational readiness.
• Define engineering patterns that speed up delivery and improve quality, maintainability and scalability.
• Explain complex technical ideas and model results to technical and non-technical audiences.
• Turn business requirements into scalable technical solutions.
• Handle ambiguity, suggest practical approaches and win stakeholder support through technical judgment.

Requirements

• 7+ years in software engineering, machine learning engineering, AI engineering, data science or related technical fields.
• 3+ years delivering AI and machine learning solutions in production.
• Experience building enterprise decision-support solutions for business and operational decisions.
• Production Python development.
• Production experience with Microsoft Azure and Databricks, plus Azure Data Services.
• Spark/PySpark.
• Machine learning tools such as XGBoost, and optimization platforms such as Gurobi or comparable tools.
• Work with large-scale enterprise datasets, both structured and unstructured.
• Experience designing, developing and supporting cloud-native applications, distributed systems, APIs and microservices, data-intensive applications and data warehousing.
• MLOps practice covering model deployment, monitoring, lifecycle management and observability.
• CI/CD pipelines, automated testing, deployment automation and operational readiness.
• Experience developing, deploying and supporting forecasting, predictive, recommendation, optimization or other business decision-support models in production.
• Experience building generative AI applications with LLMs, prompt engineering and RAG.
• Experience building and deploying AI agents and agentic workflows with frameworks such as LangGraph, Semantic Kernel, AutoGen, Azure AI Agent Service, OpenAI Agents SDK or similar.
• Ability to translate business requirements into technical solutions.
• Ability to communicate technical decisions and model outcomes to technical and non-technical stakeholders.
• Ability to manage competing priorities and recommend practical delivery approaches in ambiguous settings.
• Tags listed for the role: budget management, communication, data analytics, DOT regulations, group problem solving, JDA (inactive), Microsoft Office, Microsoft Power Business Intelligence (PBI), Oracle Transportation Management, SAP Manufacturing Execution (SAP ME), supply chain, Tableau (Software), transportation logistics, Transportation Management Systems (TMS), transportation planning.

Conditions

Pay range in the United States: $152,000 - $178,300 USD. Base pay may vary by geography, job-related knowledge, skills and experience.

Location: United States of America, city: Atlanta.

Travel: 00% - 25%.

Relocation provided: No.

Application closes November 15, 2026.

The company will not sponsor employment visas for this role, including H-1B and other employment-based nonimmigrant visas. Applicants must already be authorized to work full time in the United States and must not need sponsorship to keep working there legally.

The company is an equal opportunity employer and does not discriminate on protected characteristics.

Benefits

• Medical, financial and/or other benefits are offered, depending on the position.
• Annual incentive reference value percentage: 15. This is a market-based value for the role, set at the middle of the range, reflecting performance at target.