Sr. AI Engineer-Promo Optimisation
Target India
Bengaluru, Karnataka, IndiaSENIOR
AIMachine LearningRetail
Job Description
As a Senior AI Engineer, you will build production-grade AI/ML capabilities to optimize marketing ecosystems.
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.
Qualifications
- 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.
- 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.
- 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.
- 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.
- 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.
- Strong debugging, testing, documentation, and production support capabilities.
- Ability to collaborate with Data Science, Product, Engineering, and business teams to deliver scalable AI solutions.
- Exposure to agentic AI systems, including multi-agent workflows, planning, tool usage, orchestration frameworks, and autonomous or semi-autonomous decision-making patterns.
- Life at Target- https://india.target.com/
Nice to have
- Experience with event-driven architectures
- Knowledge of Generative AI
Benefits
- Career growth opportunities
- Collaborative work environment