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Sr Engineer - Ad Tech

Target
Bengaluru, IND
Full-time
Senior · 4–7 years experience
Onsite
Discovered 1 weeks ago
dockerjavakafkakotlinkubernetesredis

Full Job Posting

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.
  • 4–7 years of software engineering experience, with meaningful experience as a senior individual contributor.

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.
  • 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.

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