Senior AI Engineer

We are hiring a Senior AI Engineer to design, build, and operate enterprise AI systems across our client portfolio. You will work end-to-end across the AI stack — from inference engines and platform infrastructure (vLLM, KV cache, Dynamo-style serving, GPU-accelerated AI Factory platforms) up through application-level engineering (RAG pipelines, agent workflows, prompt engineering, evaluation methodology). This role is for an engineer who can lead workstreams independently, mentor more junior engineers, and serve as the technical authority that clients trust to deliver production AI outcomes. You'll engage directly with client architects, data scientists, application teams, and executives — and you'll leave each engagement having raised both the client's capability and BlueAlly 's practice. Key Responsibilities: Lead end-to-end design, build, and operation of AI systems on AI Factory platforms (HPE PCAI, Dell AI Factory, Nutanix Enterprise AI, and adjacent ecosystem layers) across multiple client engagements. Engineer and tune LLM inference serving stacks — primary depth in vLLM with breadth across the inference ecosystem — for client latency, throughput, and cost targets. Tune inference performance through KV cache management, paged attention, batching strategies, and Dynamo-based disaggregated serving. Architect and operate MLOps pipelines covering model lifecycle, registries, deployment, rollback, and observability. Design and engineer RAG applications on top of vector databases — chunking strategies, retrieval tuning, reranking, citation handling, and context-window management. Build and tune prompt-engineering patterns at production scale — system prompts, structured output, tool and function calling. Design and maintain LLM evaluation harnesses — golden sets, regression suites, and online quality metrics. Engineer high-performance storage and networking for AI workloads — parallel filesystems, object storage tiers, and high-throughput, low-latency RDMA fabrics. Operate Kubernetes clusters underpinning AI workloads — namespaces, RBAC, resource quotas, network policies, storage classes, and ingress. Build and maintain container images, registries, and CI/CD pipelines for AI/ML services. Implement monitoring, alerting, logging, and capacity planning across the AI stack. Harden environments to meet client security and compliance requirements. Lead troubleshooting across bare metal, BIOS/firmware, OS, containers, GPUs, frameworks, and models. Engage...

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