Sr. AI Engineer-MarTech

Overview

Build and scale production-grade AI/ML capabilities that power Target's promo optimization and personalized marketing ecosystem within the MarTech team (Calibrate & Incentives).

What you'll do

• 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.

What you'll need

• 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.

Nice to have

• 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.

Details

• Location: Bangalore, India.