Sr. AI Engineer-Promo Optimisation

About Us As a Fortune 50 company with more than 400,000 team members worldwide, Target is an iconic brand and one of America’s leading retailers. Joining Target means promoting a culture of mutual care and respect while striving to make the most meaningful and positive impact. Becoming a Target team member means joining a community that values different voices and lifts each other up. Here, we believe your unique perspective is important, and you’ll build relationships by being authentic and respectful. Overview About Target in India At Target, we have a timeless purpose and a proven strategy. And that hasn’t happened by accident. Some of the best minds from different backgrounds come together at Target to redefine retail in an inclusive learning environment that values people and delivers world-class outcomes.That winning formula is especially apparent in Bengaluru, where Target in India operates as a fully integrated part of Target’s global team and has more than 5,000 team members supporting the company’s global strategy and operations. Pyramid Overview A role with Target Data Science & Engineering means the opportunity to help develop, deploy, and operate state-of-the-art AI, machine learning, and optimization systems that use data at scale to automate and improve business decisions. Whether you work across Machine Learning, Optimization, Statistics, AI Engineering, or MLOps, you’ll be challenged to harness Target’s impressive data breadth to build intelligent systems that power solutions for partners in Marketing, Supply Chain Optimization, Personalization, Network Security, Merchandising, and Guest Experience. Every team member in Target Data Science & Engineering is expected to contribute to high-quality modeling and engineering outcomes, write maintainable and performant production code, apply strong software engineering practices, and use retail domain knowledge to create measurable business impact. Team Overview The Promo Optimization team (Calibrate & Incentives) builds intelligent decisioning capabilities that power personalized promotions and offers for Target guests. The team is responsible for developing and scaling AI/ML systems that help determine which guests should receive which offers, at what depth, through which channels, and under what business constraints. Promotions are a critical lever for guest engagement, loyalty, incremental sales, and enterprise growth. 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. About the Role As a Senior AI Engineer , you will help build and scale production-grade AI/ML capabilities that power Target’s promo optimization and personalized marketing ecosystem. You will partner closely with Data Scientists, Product Managers, Engineers, Analysts, and business stakeholders to turn AI ideas, models, and optimization strategies into reliable, scalable, secure, and high-performing production systems. This role is ideal for engineers who enjoy building at the intersection of AI, software engineering, data platforms, and MLOps. You will work hands-on with Python, distributed data pipelines, Kafka and event-driven architectures, APIs, databases, model deployment, ML workflow orchestration, observability, and production support. You will also explore and apply emerging AI technologies such as Generative AI, LLMs, RAG, AI agents, model evaluation frameworks, and intelligent workflow automation to solve real retail problems at scale. We are looking for someone with strong software engineering fundamentals, practical AI/ML deployment experience, and the ability to balance innovation with reliability, scalability, security, and maintainability. If you enjoy solving complex problems, building enterprise-grade AI platforms, and shaping the future of AI-powered retail decisioning, this is a great opportunity to make meaningful impact at Target. Key Responsibilities Build production-grade AI/ML applications, services, and platforms using Python and modern engineering practices, with a focus on clean code, testing, documentation, reliability, scalability, and maintainability. Design and develop scalable data and ML pipelines for batch, streaming, and near-real-time processing using distributed data frameworks, Kafka or event-driven architecture, workflow orchestration tools, and enterprise data platforms. Implement end-to-end model training, evaluation, deployment, inference, monitoring, and lifecycle management workflows that can scale across large datasets and high-impact enterprise use cases. 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 that expose AI/ML capabilities to downstream applications and business workflows. Design scalable inference systems for promotion decisioning, segmentation, redemption prediction, offer ranking, campaign simulation, and personalized marketing use cases. Work with SQL, NoSQL, object stores, feature stores, and distributed data systems to store, retrieve, transform, and manage structured and unstructured data for AI/ML applications. Support production deployment and release management through CI/CD, containerization, automated testing, model versioning, automated validation, release controls, rollback strategies, and environment management. Implement MLOps capabilities including feature pipelines, model registries, experiment tracking, automated retraining, performance monitoring, data drift detection, model drift detection, lineage, governance, and reproducibility. 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. Evaluate and integrate Generative AI and LLM components, including prompt workflows, RAG pipelines, embeddings, vector databases, model evaluation, guardrails, safety controls, and orchestration patterns where applicable. Explore agentic AI workflows, including planning, tool use, multi-step reasoning, workflow orchestration, and human-in-the-loop patterns for internal productivity and decision-support use cases. Contribute to design reviews, architecture discussions, code reviews, operational readiness reviews, and engineering standards for AI/ML systems. Troubleshoot production issues across data pipelines, model services, APIs, optimization workflows, and downstream integrations; identify root causes and implement durable fixes. Create reusable frameworks, libraries, templates, and best practices that improve AI engineering velocity and quality across the team. Communicate technical designs, trade-offs, system behavior, risks, and production performance clearly to technical and non-technical stakeholders. About You Bachelor’s degree in Computer Science, Engineering, Data Science, Machine Learning, Mathematics, Statistics, or a related technical field, or equivalent practical experience. 4+ years of experience in software engineering, AI engineering, machine learning engineering, data engineering, MLOps, or production ML systems. Strong hands-on programming experience in Python, with the ability to write modular, maintainable, well-tested, production-quality code. Experience building and deploying end-to-end AI/ML pipelines, including data preparation, feature engineering, model training, model evaluation, model deployment, inference, monitoring, and lifecycle management. Strong understanding of MLOps practices, including CI/CD for ML, model versioning, experiment tracking, automated validation, model registry, retraining workflows, deployment automation, and production monitoring. Experience designing and operating scalable model inference systems, batch scoring pipelines, APIs, microservices, or event-driven ML integrations. Experience working with distributed data processing systems such as Spark, Hadoop/Hive, or equivalent large-scale data platforms. Experience with SQL and one or more database technologies, including relational databases, NoSQL databases, object stores, or feature stores. Strong software engineering fundamentals, including data structures, algorithms, system design, API design, testing, code reviews, error handling, debugging, and documentation. Working knowledge of machine learning concepts, model evaluation, feature engineering, model serving, and common ML frameworks. Experience with containerization, orchestration, cloud platforms, workflow schedulers, and modern DevOps practices. Good understanding of observability and reliability for AI/ML systems, including monitoring, alerting, logging, performance tracking, debugging, and root-cause analysis. Ability to partner effectively with Data Scientists and translate experimental models or notebooks into scalable production systems. Ability to work in ambiguous problem spaces, break down complex systems, and deliver high-quality solutions against business timelines. Excellent written and verbal communication skills, with the ability to explain technical concepts, trade-offs, and system behavior to both technical and non-technical audiences. Must-Have Skills Strong Python engineering e xperience with production-quality coding practices. Hands-on experience building and deploying AI/ML pipelines or ML-powered applications. Practical experience with MLOps, model deployment, CI/CD, monitoring, and lifecycle management. Experience with large-scale d ata processing using SQL and distributed data platforms. Experience building APIs, services, batch jobs, or event-driven integrations for AI/ML use cases. Strong debugging, testing, documentation, and production support capabilities. Ability to collaborate with Data Science, Product, Engineering, and business teams to deliver scalable AI solutions. Preferred / Good-to-Have Skills Experience building applications using Generative AI and LLMs, including prompt engineering, RAG architectures, embeddings, vector databases, evaluation frameworks, and model orchestration. Exposure to agentic AI systems , including multi-agent workflows, planning, tool usage, orchestration frameworks, and autonomous or semi-autonomous decision-making patterns. 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. Experience working with optimization models or decisioning systems, including linear programming, mixed-integer programming, simulation, heuristics, or constraint-based systems. Experience building reusable AI platforms, shared ML services, feature platforms, model-serving platforms, or internal developer tools used across multiple teams. Experience designing high-throughput, low-latency, cost-efficient inference systems for production workloads. Experience with cloud-based ML platforms, Kubernetes, Docker, Airflow, model registries, feature stores, or workflow orchestration tools. Experience with ML frameworks and tools such as scikit-learn, XGBoost, TensorFlow, PyTorch, MLflow, Kubeflow, Ray, LangChain, LlamaIndex, or similar technologies. Experience with experimentation platforms, A/B testing infrastructure, causal measurement systems, or business impact measurement. Know More About Us here: Life at Target - Benefits - Culture-