Senior AI Engineer_ (Backend)

Tredence Inc.; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

eBay Global Consumer Insights seeks an experienced Senior AI Engineer (Backend) to design, build, and scale production AI agents that power insights generation and synthesis.

You will collaborate across product, research, and engineering teams to deliver reliable, evaluation-driven AI systems with strong observability, low latency, and robust governance for responsible AI. This role offers an opportunity to shape AI strategy at scale in a fast-paced environment.

Qualifications

• 8–12 years of software/AI engineering experience with production-grade systems.
• 4+ years building and deploying AI-centric systems.
• 2+ years hands-on with LLM-based agents and multi-agent orchestration.
• Expert in Python; familiar with TypeScript/Node.js.
• Experience with cloud providers (AWS/Azure/GCP) and containerization.

Responsibilities

• Design and build stateful, multi-agent AI systems for scalable insights generation.
• Collaborate with Product, Research, and stakeholders to deploy end-to-end AI solutions.
• Implement evaluation pipelines to measure performance and reduce hallucinations.
• Develop backend services with high throughput and low latency; contribute to frontend prototypes.
• Establish monitoring, observability, and deployment practices in production.

Skills

Python

TypeScript/Node.js

LLM-based agents

Multi-agent orchestration

APIs/SDKs integration

Tools

LangGraph

LlamaIndex Workflows

LangSmith

LangFuse

Arize Phoenix

Docker/Kubernetes

PyTorch

Transformers

Job description

About The Team And The Role

Global Consumer Insights (GCI) sits within eBay’s Growth organization and ignites growth by bringing the voice of the consumer into client most important decisions. As AI-enabled tools become central to how insights are created, synthesized, and activated, GCI is investing in responsible, practical capabilities that help researchers and business partners move faster, work smarter, and deliver greater impact.

Role Description

Senior AI Engineer (Backend)

Experience: 8-12 Years

AI Engineer – Production AI Agents

As an AI Engineer focused on Production AI Agents, you will partner closely with Product, Research, Engineering, and cross-functional stakeholders to design, build, and scale AI-powered systems that enhance how insights are generated and operationalized. This role emphasizes moving beyond experimentation to deliver reliable, evaluation-driven AI solutions that integrate seamlessly into real workflows. You will play a key role in shaping GCI’s AI ecosystem by building robust agent architectures, ensuring production readiness, and continuously improving system performance and trust.

What You Will Accomplish

• Design and build stateful, multi-agent AI systems using modern orchestration frameworks, enabling scalable and reliable workflows for insights generation and synthesis.
• Collaborate with Product, Research, and business stakeholders to translate requirements into end-to-end AI solutions, from proof of concept through evaluation and production deployment.
• Solid understanding of Retrieval-Augmented Generation (RAG), including hybrid search, re-ranking, and advanced retrieval techniques.
• Implement evaluation frameworks and pipelines (e.g., LLM-as-a-Judge, automated benchmarks) to measure system performance, reliability, and quality before and after release.
• Develop and maintain scalable backend services for high-throughput, low-latency workloads, while contributing to lightweight frontend components to deliver functional prototypes and internal tools.
• Optimize batching, streaming, caching, and request orchestration in distributed and async environments.
• Improve production systems across latency, throughput, reliability, observability, and unit economics.
• Partner with infrastructure teams to leverage GPU-enabled and cloud-native environments effectively.
• Establish monitoring, tracing, and observability practices for complex AI systems, ensuring performance, reliability, and debuggability in production.
• Develop reusable platform components, MCPs / APIs, and best practices for AI application development.
• Drive a pragmatic, evaluation-driven approach to adopting new AI technologies, balancing innovation with reliability and business impact.
• Stay current with advancements in AI (e.g., reasoning models, SLMs, prompting strategies) and apply them to improve systems and workflows.
• Partner with cross-functional teams to ensure AI solutions align with responsible AI, privacy, and security standards.

What You Will Bring

• 8 to 12 years of experience in software engineering, AI/ML engineering, or full-stack development, with hands-on ownership of building and deploying production-grade applications or platforms.
• 4+ years of focused experience building and deploying AI-centric systems.
• 2+ years of hands-on experience with LLM-based agents, autonomous workflows, or multi-agent orchestration.
• Strong full-stack engineering experience, with deep expertise in Python and familiarity with TypeScript or Node.js.
• Hands-on experience with AI orchestration frameworks such as LangGraph, LlamaIndex Workflows, or similar tools.
• Solid understanding of Retrieval-Augmented Generation (RAG), including hybrid search, re-ranking, and advanced retrieval techniques.
• Experience implementing observability and tracing for AI systems (e.g., LangSmith, LangFuse, Arize Phoenix).
• Production experience with modern ML tooling and frameworks (for example: PyTorch, Transformers, scikit-learn).
• Proven experience taking AI-powered products from prototype to production with strong maintainability and operational quality.
• Proven ability to design and execute evaluation pipelines and testing frameworks to ensure reliability and reduce hallucinations.
• Experience working with APIs/SDKs from major model providers (OpenAI, Anthropic, Gemini) and open-source models.
• Experience deploying and managing services on cloud platforms (AWS, Azure, or GCP) and using containerization (Docker/Kubernetes).
• Familiarity with CI/CD pipelines and DevOps practices.
• Strong collaboration and communication skills, with the ability to work effectively across technical and non-technical teams.

Preferred Qualifications

• Experience with Spring-based service development.
• Familiarity with big data and processing ecosystems (for example: Spark, Hadoop).
• Experience with streaming systems (for example: Kafka, Flink, Beam).
• Experience with RAG pipelines, vector stores, tool-use frameworks, and multimodal model integration.
• Exposure to GPU optimization and performance tuning (for example: CUDA, inference optimization techniques).
• Experience building conversational AI systems (intents, entities, dialog flows, and interaction design).
• Familiarity with prompt optimization tools such as DSPy.
• Proficiency with vector databases (Pinecone, Weaviate, Qdrant, pgvector).
• Exposure to voice agents or multimodal AI systems.
• Experience with graph databases (e.g., Neo4j) or GraphRAG approaches.
• Foundational knowledge of machine learning or model fine-tuning.