Sr. AI Engineer-Promo Optimisation

Target

Job Location:

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: 2 days ago

Vacancies: 1 Vacancy

Job Summary

About Us
As a Fortune 50 company with more than 400000 team members worldwide Target is an iconic brand and one of Americas 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 youll 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 hasnt 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 winning formula is especially apparent in Bengaluru where Target in India operates as a fully integrated part of Targets global team and has more than 5000 team members supporting the companys 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 youll be challenged to harness Targets 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 Targets 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

Bachelors 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 experience 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 data 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- Experience:
Senior IC

About UsAs a Fortune 50 company with more than 400000 team members worldwide Target is an iconic brand and one of Americas 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 mem...

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

Bachelors 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 experience 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 data 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.