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Lead Software Engineer- AI Agent Developer

JPMorgan Chase
Bengaluru, IND
Full-time
Lead · 8+ years experience
Onsite
Discovered 5 days ago
javaneo4jpythontypescript

Full Job Posting

Job responsibilities

  • Architect and ship autonomous agent systems that are scalable, resilient, and production-grade (latency, reliability, security, and uptime).
  • Design agentic architectures (planner/executor patterns, multi-agent orchestration, tool use, retrieval, and workflow execution).
  • Build robust tool interfaces and tool ecosystems (APIs, function calling patterns, permissions, and safe execution).
  • Engineer context, memory, and state management strategies (short/long-term memory, summarization, retrieval, grounding, and caching).
  • Own model evaluation and quality measurement (offline/online evals, regression testing, and failure-mode analysis).
  • Implement end-to-end observability for LLM systems (tracing, metrics, logging, prompt/tool telemetry, incident response).
  • Drive cost and performance optimization across models and infrastructure (throughput, batching, caching, routing, quantization, and inference efficiency).
  • Mentor engineers and set engineering standards for production AI systems (code quality, design reviews, and operational excellence).
  • 8+ years of software engineering experience, including leading design and delivery of large-scale production systems.
  • Demonstrated experience building and operating LLM/agentic systems in production with strict latency and reliability requirements.
  • Strong understanding of foundation models, LLM behavior, prompting strategies, and tool-augmented generation patterns.
  • Strong understanding of Knowledge graph (Neo4J preferred), Open Knowledge formats (OKFs)
  • Proven expertise in context and memory engineering, model evaluation methodologies, and quality measurement.
  • Experience implementing observability for distributed systems and AI applications (tracing/metrics/logs, dashboards, alerting, on-call readiness).
  • Strong programming skills in one or more of: Python, Java, Go, or TypeScript; plus strong API and distributed-systems fundamentals.
  • Familiarity with retrieval systems, vector databases, and hybrid search patterns.
  • Agentic AI architecture: planner/executor patterns, multi-agent orchestration, tool-use design, retrieval workflows
  • LLM/foundation model expertise: prompting strategies, tool-augmented generation, model behavior understanding
  • Context & memory engineering: short/long-term memory, summarization, retrieval grounding, caching strategies
  • Knowledge graphs: Neo4J (preferred), Open Knowledge Formats (OKFs)
  • Retrieval systems: vector databases, hybrid search patterns

Required qualifications, capabilities and skills

  • 8+ years of software engineering experience, including leading design and delivery of large-scale production systems.
  • Demonstrated experience building and operating LLM/agentic systems in production with strict latency and reliability requirements.
  • Strong understanding of foundation models, LLM behavior, prompting strategies, and tool-augmented generation patterns.
  • Strong understanding of Knowledge graph (Neo4J preferred), Open Knowledge formats (OKFs)
  • Proven expertise in context and memory engineering, model evaluation methodologies, and quality measurement.
  • Experience implementing observability for distributed systems and AI applications (tracing/metrics/logs, dashboards, alerting, on-call readiness).
  • Strong programming skills in one or more of: Python, Java, Go, or TypeScript; plus strong API and distributed-systems fundamentals.
  • Familiarity with retrieval systems, vector databases, and hybrid search patterns.

Preferred qualifications, capabilities and skills

  • Agentic AI architecture: planner/executor patterns, multi-agent orchestration, tool-use design, retrieval workflows
  • LLM/foundation model expertise: prompting strategies, tool-augmented generation, model behavior understanding
  • Context & memory engineering: short/long-term memory, summarization, retrieval grounding, caching strategies
  • Knowledge graphs: Neo4J (preferred), Open Knowledge Formats (OKFs)
  • Retrieval systems: vector databases, hybrid search patterns

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