{"id":1260688,"url":"https://alion.io/job/blueally-senior-artificial-intelligence-engineer","title":"Senior Artificial Intelligence Engineer","company":{"id":1867026,"name":"BlueAlly","domain":"blueally.com","url":"https://alion.io/company/blueally","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"ADP","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-27T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"DNS","optional":false},{"name":"Docker","optional":false},{"name":"Function Calling","optional":false},{"name":"GitOps","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TCP/IP","optional":false},{"name":"Tool Use","optional":false},{"name":"vLLM","optional":false},{"name":"Ansible","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"FedRAMP","optional":true},{"name":"Fine-tuning","optional":true},{"name":"GCP","optional":true},{"name":"Helm","optional":true},{"name":"HIPAA","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"LDAP","optional":true},{"name":"LoRA","optional":true},{"name":"Model Distillation","optional":true},{"name":"PEFT","optional":true},{"name":"Prompt Caching","optional":true},{"name":"QLoRA","optional":true},{"name":"Ray","optional":true},{"name":"SFT","optional":true},{"name":"SOC 2","optional":true},{"name":"Terraform","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-06-05T19:09:00Z","employer_posted_date":"2026-06-05","last_verified_at":"2026-09-27T04:34:40Z","board_verified":true,"closed_at":null,"days_open":114,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":113},"description":"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).\nThis 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.\nKey Responsibilities\nLead 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.\nEngineer and tune LLM inference serving stacks - primary depth in vLLM with breadth across the inference ecosystem - for client latency, throughput, and cost targets.\nTune inference performance through KV cache management, paged attention, batching strategies, and Dynamo-based disaggregated serving.\nArchitect and operate MLOps pipelines covering model lifecycle, registries, deployment, rollback, and observability.\nDesign and engineer RAG applications on top of vector databases - chunking strategies, retrieval tuning, reranking, citation handling, and context-window management.\nBuild and tune prompt-engineering patterns at production scale - system prompts, structured output, tool and function calling.\nDesign and maintain LLM evaluation harnesses - golden sets, regression suites, and online quality metrics.\nEngineer high-performance storage and networking for AI workloads - parallel filesystems, object storage tiers, and high-throughput, low-latency RDMA fabrics.\nOperate Kubernetes clusters underpinning AI workloads - namespaces, RBAC, resource quotas, network policies, storage classes, and ingress.\nBuild and maintain container images, registries, and CI/CD pipelines for AI/ML services.\nImplement monitoring, alerting, logging, and capacity planning across the AI stack.\nHarden environments to meet client security and compliance requirements.\nLead troubleshooting across bare metal, BIOS/firmware, OS, containers, GPUs, frameworks, and models.\nEngage directly with client stakeholders - technical and executive - to communicate status, root cause, options, and recommendations.\nMentor and code-review work from less senior engineers; raise the technical bar of every engagement you join.\nAuthor runbooks, reference architectures, and knowledge base content; lead client knowledge transfer and enablement sessions.\nParticipate in on-call rotation and incident response for production AI workloads.\nContribute reusable patterns, tooling, and reference designs back to the practice.\nRequired Qualifications\nExperience: 7+ years of software, data, or infrastructure engineering, with 3+ years specifically working with modern AI / LLM systems.\nSoftware engineering: Production-quality Python at engineering level - testing, code review, version control fluency, and shipping code that other engineers depend on.\nLinux engineering: Deep production Linux experience, including system internals, performance tuning, and troubleshooting.\nContainers: Deep proficiency with Docker - image build, registry management, runtime tuning, and container security.\nHardware fundamentals: Strong server-platform skills including CPU/GPU topologies, PCIe, BMC management, BIOS/firmware lifecycle, and physical-to-logical troubleshooting.\nAI Factory platforms: Hands-on experience deploying and operating one or more of HPE PCAI, Dell AI Factory, or Nutanix Enterprise AI.\nInference stack - vLLM: Production experience deploying, tuning, and operating vLLM.\nInference stack breadth: Working knowledge of multiple inference and model-serving frameworks beyond vLLM, with the ability to choose and tune the right tool for each workload.\nHigh-performance storage and networking: 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.\nMLOps: Practical experience operating MLOps tooling and patterns - model registries, deployment pipelines, GitOps, lineage, and rollback.\nVector databases and RAG: Hands-on experience deploying, tuning, and integrating vector databases and RAG pipelines, including the application-level engineering that sits on top of them.\nPrompt engineering and tool use: Production experience designing system prompts, structured output, function calling, and tool-using LLM patterns.\nEvaluation methodology: Demonstrated experience designing LLM evaluation harnesses - golden sets, regression suites, and quality/cost metrics.\nClient-facing skills: Demonstrated ability to engage directly with client stakeholders - running working sessions, presenting recommendations, and translating technical detail for non-technical audiences.\nCommunication: Strong written and verbal communication - clear reference architectures, runbooks, and incident reports.\nMentorship: Track record of mentoring more junior engineers and raising team technical quality through code review and pairing.\nNetworking fundamentals: TCP/IP, DNS, load balancing, VLANs, and firewall administration.\nMulti-client delivery: Comfort working across multiple concurrent client environments and managing competing priorities under SLA.\nPreferred Qualifications\n GPU operations: Experience with GPU drivers, CUDA toolchains, GPU partitioning (MIG/vGPU), and GPU-level monitoring.\nNVIDIA AI Enterprise: Deployment and operations experience with the NVAIE software stack.\nRay: Familiarity with Ray for distributed training and inference scaling.\nKubernetes: Working knowledge of Kubernetes administration - Helm, ingress, RBAC, storage classes.\n Identity and access: Integrating SSO and enterprise identity (LDAP, AD, OIDC/SAML), secrets management, tenant isolation.\nFine-tuning: Familiarity with LoRA/QLoRA/PEFT and supervised fine-tuning workflows.\nToken economics: Experience optimizing inference cost - caching, prompt caching, model routing, and distillation.\nMSP / multi-tenant operations: Service-provider experience including chargeback/showback and tenant isolation patterns.\nCompliance frameworks: SOC 2, HIPAA, FedRAMP, FISMA, or CMMC environments.\nPublic cloud and hybrid: Working experience with one or more public clouds and hybrid architectures.\nInfrastructure as Code: Terraform, Ansible, Helm, or similar.\nCertifications (Preferred)\nCertified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD).\nCloud certifications - AWS, Azure, or Google Cloud.\nLinux certifications - RHCE, RHCSA, or LFCS.\nNVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO) or higher NVIDIA certifications.\nHPE, Dell Technologies, or Nutanix platform certifications.\nWhat Sets You Apart\nGenuine curiosity about how AI systems work end-to-end - from kernel and GPU up through frameworks and models.\nTrack record of restoring production AI services under pressure.\nAbility to translate complex technical concepts into clear, client-facing communication.\nComfort with ambiguity and rapid change in the AI/LLM ecosystem.\nService-oriented mindset: you treat each client environment as if it were your own.\nBias toward leaving the practice better than you found it - patterns, tooling, and reference designs.\nAbout BlueAlly\nBlueAlly is a leading provider of IT services and solutions, helping organizations conquer IT complexity across cloud, cybersecurity, infrastructure, data, and application modernization. Headquartered in Cary, North Carolina, with delivery teams across the United States and globally, BlueAlly serves clients ranging from mid-market enterprises to large public-sector and commercial organizations.\nFounded in 2011, BlueAlly delivers across the full technology lifecycle - from strategy and design through implementation, managed services, and continuous optimization. The company is recognized on CRN's Tech Elite 150 and MSP 500 lists and partners deeply with leading technology vendors. As enterprise AI moves from pilot to production, BlueAlly is investing in the people, platforms, and practices required to deliver AI Factory outcomes for our clients - and this role is at the center of that investment.\nEqual Employment Opportunity\nBlueAlly is an Equal Opportunity Employer. We are committed to building a diverse and inclusive workforce and to making employment decisions based on merit, qualifications, and business need. BlueAlly does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy), national origin, age, disability, genetic information, sexual orientation, gender identity or expression, marital status, veteran status, or any other protected characteristic under applicable federal, state, or local law.\n\nBlueAlly provides reasonable accommodations to qualified applicants and employees with disabilities. 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