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