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About This Role
Company 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.
Job 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).
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 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.
Qualifications
- Experience: 7\+ years of software, data, or infrastructure engineering, with 3\+ years specifically working with modern AI / LLM systems.
- Software engineering: Production\-quality Python at engineering level — testing, code review, version control fluency, and shipping code that other engineers depend on.
- Linux engineering: Deep production Linux experience, including system internals, performance tuning, and troubleshooting.
- Containers: Deep proficiency with Docker — image build, registry management, runtime tuning, and container security.
- Hardware fundamentals: Strong server\-platform skills including CPU/GPU topologies, PCIe, BMC management, BIOS/firmware lifecycle, and physical\-to\-logical troubleshooting.
- AI Factory platforms: Hands\-on experience deploying and operating one or more of HPE PCAI, Dell AI Factory, or Nutanix Enterprise AI.
- Inference stack — vLLM: Production experience deploying, tuning, and operating vLLM.
- Inference 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.
- High\-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.
- MLOps: Practical experience operating MLOps tooling and patterns — model registries, deployment pipelines, GitOps, lineage, and rollback.
- Vector 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.
- Prompt engineering and tool use: Production experience designing system prompts, structured output, function calling, and tool\-using LLM patterns.
- Evaluation methodology: Demonstrated experience designing LLM evaluation harnesses — golden sets, regression suites, and quality/cost metrics.
- Client\-facing skills: Demonstrated ability to engage directly with client stakeholders — running working sessions, presenting recommendations, and translating technical detail for non\-technical audiences.
- Communication: Strong written and verbal communication — clear reference architectures, runbooks, and incident reports.
- Mentorship: Track record of mentoring more junior engineers and raising team technical quality through code review and pairing.
- Networking fundamentals: TCP/IP, DNS, load balancing, VLANs, and firewall administration.
- Multi\-client delivery: Comfort working across multiple concurrent client environments and managing competing priorities under SLA.
Preferred Qualifications:
- GPU operations: Experience with GPU drivers, CUDA toolchains, GPU partitioning (MIG/vGPU), and GPU\-level monitoring.
- NVIDIA AI Enterprise: Deployment and operations experience with the NVAIE software stack.
- Ray: Familiarity with Ray for distributed training and inference scaling.
- Kubernetes: Working knowledge of Kubernetes administration — Helm, ingress, RBAC, storage classes.
- Identity and access: Integrating SSO and enterprise identity (LDAP, AD, OIDC/SAML), secrets management, tenant isolation.
- Fine\-tuning: Familiarity with LoRA/QLoRA/PEFT and supervised fine\-tuning workflows.
- Token economics: Experience optimizing inference cost — caching, prompt caching, model routing, and distillation.
- MSP / multi\-tenant operations: Service\-provider experience including chargeback/showback and tenant isolation patterns.
- Compliance frameworks: SOC 2, HIPAA, FedRAMP, FISMA, or CMMC environments.
- Public cloud and hybrid: Working experience with one or more public clouds and hybrid architectures.
- Infrastructure as Code: Terraform, Ansible, Helm, or similar.
Certifications (Preferred):
- Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD).
- Cloud certifications — AWS, Azure, or Google Cloud.
- Linux certifications — RHCE, RHCSA, or LFCS.
- NVIDIA\-Certified Associate: AI Infrastructure and Operations (NCA\-AIIO) or higher NVIDIA certifications.
- HPE, Dell Technologies, or Nutanix platform certifications.
What Sets You Apart:
- Genuine curiosity about how AI systems work end\-to\-end — from kernel and GPU up through frameworks and models.
- Track record of restoring production AI services under pressure.
- Ability to translate complex technical concepts into clear, client\-facing communication.
- Comfort with ambiguity and rapid change in the AI/LLM ecosystem.
- Service\-oriented mindset: you treat each client environment as if it were your own.
- Bias toward leaving the practice better than you found it — patterns, tooling, and reference designs.
Additional Information About BlueAlly
BlueAlly 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.
Founded 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.
Equal Employment Opportunity
BlueAlly 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.
BlueAlly provides reasonable accommodations to qualified applicants and employees with disabilities. If you require an accommodation to participate in the application or interview process, please contact our People team.
Salary Context
This $180K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At BlueAlly, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($190K) sits 6% above the category median. Disclosed range: $180K to $200K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
BlueAlly AI Hiring
BlueAlly has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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