Join us to advance your software engineering career while building impactful technology solutions. Grow your skills and make a difference with a collaborative team.
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).
- 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
