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. At Target, we have a timeless purpose and a proven strategy, and that hasn’t happened by accident. Some of the best minds from diverse 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 4,000 team members supporting the company’s global strategy and operations.
Joining Target means promoting a culture of mutual care and respect and striving to make the most meaningful and positive impact. Becoming a Target team member means joining a community that values diverse backgrounds. We believe your unique perspective is important, and you'll build relationships by being authentic and respectful. At Target, inclusion is part of our core value. We aim to create equitable experiences for all, regardless of their dimensions of difference. As an equal opportunity employer, Target provides diverse opportunities for everyone to grow and win.

About Team
Target Tech is on a mission to build the systems, tools, and platforms that enable our teams, guests, and partners to operate effectively and deliver meaningful experiences at scale.
Our AI Platform team is focused on building production-grade AI capabilities that can be adopted across engineering and business use cases. The team works at the intersection of modern AI, software engineering, and distributed systems to build reliable platforms that make AI useful beyond experimentation and into real production environments.
We are building capabilities around LLM applications, retrieval-augmented generation, AI agents, and tool-enabled workflows, with a strong focus on reliability, observability, scalability, and responsible engineering.

Role Overview
As a Senior Engineer - AI Platform, you will be a senior individual contributor responsible for designing and building production-grade AI capabilities and helping shape the technical direction of the platform.
You will work hands-on across LLM applications, RAG, AI agents, MCP, Java/Kotlin and distributed systems, translating emerging AI capabilities into reliable software that can operate at scale.
The role is a combination of strong software engineering and practical AI engineering. You will be expected to make sound architectural decisions, solve complex technical problems, contribute to engineering standards, and mentor other engineers.
The key to success in this role is having real production experience building and shipping AI applications, not just experimenting with LLMs or proof-of-concepts.
Use your skills, experience, and talents to be a part of groundbreaking thinking and visionary goals.

AI Engineering & Agentic Systems

LLM Applications: Build and integrate production-grade applications using large language models, including prompt engineering, model integration, structured outputs, and inference workflows.
RAG: Design and implement Retrieval-Augmented Generation solutions covering retrieval, embeddings, metadata filtering, reranking, grounding, citations, and evaluation.
AI Agents: Build agentic workflows using agent frameworks and tool-enabled architectures, including Model Context Protocol (MCP) and multi-step agent interactions.
Agent Architecture: Design reliable patterns for agent, tool, and service interactions with clear separation of responsibilities and measurable execution behavior.
Evaluation & Quality: Build approaches to evaluate AI systems for correctness, reliability, grounding, tool usage, and regression across model and prompt changes.

Software Engineering & Architecture

System Design: Design scalable, resilient distributed systems that support AI workloads and high-volume production use cases.
Java / Kotlin Engineering: Build backend services using Java or Kotlin, Spring Boot, microservices, APIs, and cloud-native patterns.
Architecture: Drive design decisions for core platform components, balancing performance, scalability, maintainability, and operational requirements.
Code Quality & Mentorship: Raise engineering standards through thoughtful code reviews, design reviews, mentoring, and strong implementation practices.
Technical Problem Solving: Investigate and resolve complex issues across distributed services, AI workflows, performance, latency, and production reliability.

Platform Reliability & Observability

Production Reliability: Build AI systems that are resilient, fault-tolerant, and suitable for production workloads.
Observability: Design monitoring, logging, tracing, metrics, and operational signals that make AI applications measurable and diagnosable.
Performance: Identify and improve latency, throughput, resource utilization, and model/tool execution efficiency.
Operational Excellence: Participate in production readiness, incident analysis, reliability improvements, and engineering practices that reduce operational toil.

Requirements

Education: 4-year degree in Computer Science, Engineering, or equivalent practical experience.
Experience: 4–7 years of software engineering experience, with meaningful experience as a senior individual contributor.
AI Experience: Hands-on experience building and shipping LLM-powered or agentic applications into production.
LLM / GenAI: Strong experience with LLM integration, prompt engineering, structured outputs, and production AI application patterns.
RAG: Practical experience building RAG systems, including retrieval, embeddings, metadata filtering, reranking, grounding, and evaluation.
Agentic AI: Experience with AI agent frameworks, tool calling, agent orchestration, MCP, or similar agentic architectures.
Backend Engineering: Strong proficiency in Java/Kotlin and Spring Boot, with experience building APIs and distributed services.
Distributed Systems: Strong understanding of microservices, asynchronous systems, APIs, resilience, scalability, and failure handling.
Reliability & Observability: Hands-on experience building production systems with monitoring, logging, metrics, alerting, and operational readiness.
Cloud & DevOps: Experience with cloud-native environments and technologies such as Kubernetes, Docker, CI/CD, and managed cloud services.
Engineering Practices: Strong coding, debugging, design, testing, code review, and problem-solving skills.

Desired Qualifications

Experience building multi-agent systems or agent-to-agent architectures.
Experience with Model Context Protocol (MCP) or similar tool/context integration patterns.
Experience with vector databases, embedding systems, reranking, or modern retrieval infrastructure.
Experience designing AI evaluation frameworks, including deterministic evaluation, LLM-based evaluation, and production regression testing.
Experience with observability platforms and distributed tracing for AI or microservice workloads.
Experience working with Kafka, Redis, or other distributed infrastructure technologies.
Experience building internal platforms or reusable AI capabilities consumed by multiple engineering teams.
Strong understanding of AI system reliability, security, governance, and responsible engineering practices.

What Success Looks Like
In this role, success means becoming a strong technical owner for important AI platform capabilities and helping move the team from experimentation to reliable production engineering.
You will be expected to:

Own and deliver major AI platform components.
Make architectural decisions and defend them with data and engineering judgment.
Build AI capabilities that are reliable, observable, and reusable.
Establish strong engineering practices for agentic and LLM-based systems.
Mentor engineers and raise the technical bar across the team.
Help shape the platform's next generation of AI and agentic capabilities.