Senior AI Engineer

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NetCraftsmen, now BlueAlly

WebsiteLinkedInAll Job Openings

51 - 200 employees

NetCraftsmen, was acquired by BlueAlly in March 2022. At BlueAlly, our mission is to make technology more accessible, more certain, and more impactful for every organization.

📋 Description

• Design, build, and operate enterprise AI systems across our client portfolio. • Work end-to-end across the AI stack — from inference engines and platform infrastructure up through application-level engineering. • Lead end-to-end design, build, and operation of AI systems on AI Factory platforms 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. • Build and tune prompt-engineering patterns at production scale. • Engineer high-performance storage and networking for AI workloads. • Operate Kubernetes clusters underpinning AI workloads. • 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 various environments and technologies. • Engage directly with client stakeholders — technical and executive — to communicate status, root cause, options, and recommendations. • Mentor and code-review work from less senior engineers; raise the technical bar of every engagement you join. • Author runbooks, reference architectures, and knowledge base content; lead client knowledge transfer and enablement sessions. • Participate in on-call rotation and incident response for production AI workloads. • Contribute reusable patterns, tooling, and reference designs back to the practice.

🎯 Requirements

• 7+ years of software, data, or infrastructure engineering, with 3+ years specifically working with modern AI / LLM systems. • Production-quality Python at engineering level — testing, code review, version control fluency, and shipping code that other engineers depend on. • Deep production Linux experience, including system internals, performance tuning, and troubleshooting. • Deep proficiency with Docker — image build, registry management, runtime tuning, and container security. • Strong server-platform skills including CPU/GPU topologies, PCIe, BMC management, BIOS/firmware lifecycle, and physical-to-logical troubleshooting. • Hands-on experience deploying and operating one or more of HPE PCAI, Dell AI Factory, or Nutanix Enterprise AI. • Production experience deploying, tuning, and operating vLLM. • Working knowledge of multiple inference and model-serving frameworks beyond vLLM, with the ability to choose and tune the right tool for each workload. • Hands-on experience with high-throughput, low-latency storage and network fabrics for AI workloads — including RDMA-class interconnects, parallel/object storage tiers, KV cache management, and Dynamo-style disaggregated serving. • Practical experience operating MLOps tooling and patterns — model registries, deployment pipelines, GitOps, lineage, and rollback. • Hands-on experience deploying, tuning, and integrating vector databases and RAG pipelines, including the application-level engineering that sits on top of them. • Production experience designing system prompts, structured output, function calling, and tool-using LLM patterns. • Demonstrated experience designing LLM evaluation harnesses — golden sets, regression suites, and quality/cost metrics. • Demonstrated ability to engage directly with client stakeholders — running working sessions, presenting recommendations, and translating technical detail for non-technical audiences. • Strong written and verbal communication — clear reference architectures, runbooks, and incident reports. • Track record of mentoring more junior engineers and raising team technical quality through code review and pairing. • TCP/IP, DNS, load balancing, VLANs, and firewall administration. • Comfort working across multiple concurrent client environments and managing competing priorities under SLA.