Experience:

• 4+ years of experience in software engineering, AI engineering, machine learning engineering, data engineering, MLOps, or production ML systems

Qualification:

• Bachelor’s degree in Computer Science, Engineering, Data Science, Machine Learning, Mathematics, Statistics, or a related technical field, or equivalent practical experience

Role Focus:

• Build production-grade AI/ML applications, services, and platforms using Python and modern engineering practices
• Design and develop scalable data and ML pipelines for batch, streaming, and near-real-time processing
• Implement end-to-end model training, evaluation, deployment, inference, monitoring, and lifecycle management workflows
• Partner with Data Scientists to convert prototypes, notebooks, statistical models, ML models, GenAI workflows, and optimization algorithms into reliable, reusable, and production-ready systems
• Build and deploy REST APIs, microservices, model-serving endpoints, batch scoring jobs, and event-driven integrations

Additional responsibilities:

• Support production deployment and release management through CI/CD, containerization, automated testing, model versioning, automated validation, release controls, rollback strategies, and environment management
• Implement observability and reliability mechanisms, including logging, metrics, traces, dashboards, alerting, error handling, incident response, and root-cause analysis for production AI systems
• Optimize AI/ML services for latency, throughput, cost, scalability, reliability, and operational performance
• Troubleshoot production issues across data pipelines, model services, APIs, optimization workflows, and downstream integrations

Nice to have:

• Experience building applications using Generative AI and LLMs
• Exposure to agentic AI systems
• Experience implementing LLM observability, evaluation, guardrails, safety controls, and responsible AI practices for production GenAI systems
• Experience with promotion optimization, personalization, recommender systems, marketing technology, retail media, customer targeting, pricing, or offer decisioning

More skills:

distributed data pipelines, Kafka, event-driven architectures, APIs, databases, model deployment, ML workflow orchestration, observability, production support, model evaluation frameworks, intelligent workflow automation

Other:

• Target is an iconic brand and one of America’s leading retailers with more than 400,000 team members worldwide
• The Promo Optimization team builds intelligent decisioning capabilities that power personalized promotions and offers for Target guests
• The team works at the intersection of AI engineering, machine learning, operations research, experimentation, marketing science, and production platform development to optimize promotional investments while improving guest relevance and business outcomes