AI Engineer

Role Overview We are seeking a Technical Lead to own the end-to-end strategy, architecture, and operations of a production-grade LLM inference platform. This role spans commercial partnership management, distributed systems engineering, and platform automation — owning everything from GPU cluster architecture to go-to-market execution on third-party marketplaces. Key Responsibilities Platform & Partnership Leadership Lead end-to-end launch strategy for LLM inference channels on external marketplaces (e.g., OpenRouter), including business case development, pricing strategy, and partner relationship management Own vendor and partner relationships, including routine reviews and pricing negotiations for the inference stack Scale channel throughput to production volumes exceeding 10B+ tokens/day Infrastructure & Platform Engineering Design and build custom Kubernetes Operators (CRDs + controllers) to manage LLM inference deployments and benchmark runs as declarative, first-class cluster resources Automate model provisioning, GPU placement, and scaling so new models move from config commit to serving traffic without manual intervention Architect and operate multi-node distributed inference deployments over InfiniBand Implement KV-cache reuse and disaggregated prefilling (e.g., LMCache) to drive significant throughput gains (1.6×+) Build GPU autoscaling systems (e.g., KEDA-based) tied to real-time load signals Performance & Capacity Planning Build reproducible benchmarking systems (e.g., on NVIDIA aiperf) exposed as first-class CRDs across the GPU fleet Define and measure sustainable throughput per (model, chip) under strict latency SLAs (e.g., TTFT Translate benchmark data and marketplace pricing into break-even tokens/day and required per-replica TPS to drive GPU capacity planning decisions Observability & Operations Build observability systems covering TTFT, TPS, and per-API-key latency across the fleet Design and deploy AI-driven operations agents (built on in-house multi-tenant agent runtimes) for automated anomaly detection and root-cause analysis, integrating Prometheus triggers and custom MCP tools Drive incident resolution at scale, resolving hundreds of production incidents Qualifications Proven experience architecting and operating distributed GPU inference infrastructure at production scale Hands-on expertise with Kubernetes Operator development (CRDs, custom controllers) Deep familiarity with LLM serving optimization techniques (KV caching, disaggregated prefilling, autoscaling) Experience with performance benchmarking and capacity planning for GPU workloads Track record of owning commercial partnerships and pricing strategy for technical platforms Experience building or integrating AI agent systems for operational automation Strong cross-functional leadership: comfortable operating across infrastructure, product, and business strategy. Important Note: > Please share your resume in word format with dilip@helius-tech.com > Important Note: If this requirement is not a match for you please refer to your friends. > Interested professionals can reach out to me for Confidential Discussion @ +65- 9060-4050. Best Regards, Dilip Kumar Daga Vice President - Strategic Accounts Helius Technologies Pte Ltd 36, Robinson Road,#13-05, City House, Singapore 068877 DID: +(65) 6429-9407 Mobile: +(65) 9060-4050 Fax: +(65) 62222213 Email id: dilip@helius-tech.com http://helius-tech.com Registration No : R1108376 EA Licence No : 11C3373 https://www.linkedin.com/in/dilipdaga/