BlueAlly
Technology
SeniorAIEngineer
Neural analysis suggests this role is
optimal for mid candidates.
“Senior AI Engineer at BlueAlly. Skills: AI systems, LLM inference, MLOps, RAG applications. Lead AI systems design. Build AI systems”
What You'll Achieve.
Deliver production AI outcomes; Raise client capability; Raise BlueAlly practice
Industry & Context.
Troubleshooting; Root cause analysis
On-call rotation
What They're Looking For.
Must Have
7+ years software, data, or infrastructure engineering, 3+ years AI / LLM systems, Production-quality Python, Deep production Linux experience, Deep proficiency with Docker, Server-platform skills, Hands-on AI Factory platforms experience, Production vLLM experience, High-throughput, low-latency storage and network fabrics experience, Practical MLOps tooling and patterns experience, Hands-on vector databases and RAG pipelines experience, Production prompt engineering experience, Demonstrated LLM evaluation harnesses experience, Demonstrated client stakeholder engagement ability
Nice to Have
Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD), Cloud certifications, Linux certifications, NVIDIA certifications, HPE, Dell Technologies, or Nutanix platform certifications
What You'll Do.
Lead AI systems design
Engineer LLM inference serving stacks
Tune inference performance
Architect MLOps pipelines
Operate MLOps pipelines
Design RAG applications
Engineer RAG applications
Build prompt-engineering patterns
Tune prompt-engineering patterns
Design LLM evaluation harnesses
Maintain LLM evaluation harnesses
Engineer high-performance storage
Engineer high-performance networking
Operate Kubernetes clusters
Build container images
Maintain container images
Build CI/CD pipelines
Maintain CI/CD pipelines
Implement capacity planning
Engage client stakeholders
Communicate root cause
Communicate recommendations
Mentor junior engineers
Code-review junior engineers
Author reference architectures
Author knowledge base
Lead knowledge transfer
Lead enablement sessions
Participate in on-call rotation
Participate in incident response
Contribute reusable patterns
Contribute reusable tooling
Contribute reusable reference designs
How You'll Work.
Team & Collaboration
Client architects; Data scientists; Application teams; Client executives; Junior engineers
Communication Scope
Client-facing communication; Technical communication; Executive communication
Process & Methodology
Workstreams
Full Job Description
At BlueAlly, our mission is to make technology more accessible, more certain, and more impactful for every organization. From cloud to cybersecurity, infrastructure to application modernization, we thrive on cutting-edge technologies and services. Elevate the impact of technology across your enterprise with world-class expertise that produces game-changing insights. Turn complex decisions into clear opportunities with a trusted guide to technology that ensures the next digital advance will be your decisive advantage. Trade IT complexity for capability with solutions that elevate possibilities, and advance with certainty, knowing you have BlueAlly as your ally in next. BlueAlly. Conquer Complexity. 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, an
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