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We’re seeking a Lead Software Engineer (AI/ML) to build and operate scalable, low-latency autonomous agent systems in production. You’ll lead the design and delivery of agentic platforms that reliably serve 100K+ customers, combining strong software engineering fundamentals with deep hands-on expertise in foundation models and LLM-powered architectures.
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).
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Mentor engineers and set engineering standards for production AI systems (code quality, design reviews, and operational excellence).
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
