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About This Role
Overview:
What We Do
CNXN Helix Center at Connection Inc., is at the forefront of AI innovation, offering cutting\-edge solutions that redefine the boundaries of artificial intelligence and data management. We are dedicated to helping our clients navigate the complexities of AI integration, ensuring they stay ahead in a rapidly evolving technological landscape. Our commitment to excellence, innovation, and strategic growth makes us an industry leader. Visit us at www.connection.com/helix
Who We Are
Our team is made stronger by a multitude of backgrounds, experiences, and perspectives. It’s what makes Connection unique—what drives us to innovate and create technology solutions that stand apart from the crowd. We’d love for you to be a part of that fabric, to share your ideas and experiences with a team that thrives on fresh thinking, creativity, and helping others.
Why You Should Join Us
You’ll find supportive teammates and a rewarding career at Connection—plus great benefits. We take pride in supporting employees with a total rewards package that provides financial, emotional, and physical resources for you and your family. Our compensation, 401k plans, medical insurance, and other benefits are progressive and competitive. We value the importance of our employees’ emotional wellbeing. To support employees, we provide free therapy visits, mental health coaching and tools, and meditation resources. You’ll also enjoy a generous paid time off package that includes not only vacation and sick time, but also Wellness and Volunteer Time Off days.
This is a remote or hybrid opportunity preferably based near our corporate offices in Merrimack, NH, or Boston or New York metropolitan areas.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time.
Responsibilities:
The Principal AI Solutions Advisor serves as a customer\-facing technical leader responsible for shaping solution direction, leading workshops, and guiding customers on AI, data, agent, and governance strategy across Microsoft and adjacent enterprise AI platforms. The Principal AI Solutions Advisor plays a key role in pre\-sales advisory, customer discovery, executive and technical workshops, and solution development across modern AI productivity and platform ecosystems. This role helps customers evaluate and adopt technologies centered on Microsoft 365 Copilot, Copilot Studio, Power Platform, Microsoft Fabric, Azure, and related governance, security, and adoption capabilities, while also maintaining practical working knowledge of adjacent platforms such as Claude Enterprise, Claude Code, ChatGPT Enterprise, and Codex where relevant to customer needs.
The Principal Advisor partners with sellers, strategic partners, architects, and delivery teams to guide customers through technology selection, roadmap definition, architecture direction, and scoped next steps in a rapidly evolving market. This position is intended for a strong advisory and workshop leader with practical customer\-facing experience.
- Leads customer\-facing discovery sessions, executive briefings, and workshops ranging from approximately 2 to 16 hours in duration.
- Advises customers on Microsoft AI, automation, and data platforms, including Microsoft 365 Copilot, Copilot Studio, Power Platform, Microsoft Fabric, Azure AI, Azure data services, and related governance and security considerations.
Helps customers understand and compare options for AI assistants, agent platforms, enterprise coding tools, and AI governance patterns, including practical exposure to Claude Enterprise, Claude Code, ChatGPT Enterprise, and Codex.
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- Translates customer business goals, technical requirements, risk concerns, and operating constraints into practical recommendations, reference architectures, maturity assessments, and roadmap options.
- Supports pre\-sales pursuits by partnering with Connection account teams, OEMs, hyperscalers, software partners, and internal delivery resources to shape opportunities and position CNXN Helix services.
- Develops workshop outputs and reusable assets including presentation materials, assessments, decision frameworks, use\-case maps, demonstration narratives, and solution playbooks.
- Guides customers on topics such as agent strategy, Copilot readiness, data readiness, responsible AI, governance, compliance, environment strategy, identity and access, lifecycle management, and operational adoption.
- Provides practical direction on Power Platform architecture, Dataverse, environment planning, DLP, ALM, Center of Excellence concepts, and governance operating models.
- Supports customer conversations related to Microsoft Fabric, Azure analytics modernization, and data platform alignment for AI and agentic use cases.
- Maintains awareness of broader infrastructure and platform choices, including AWS, Google Cloud, Dell, HPE, and NVIDIA\-based AI infrastructure, where those choices influence customer strategy and buying decisions.
- Operates across a broad customer base spanning multiple verticals including manufacturing, healthcare, retail, financial services, education, and other industries, as well as customers of varying size and maturity.
- Builds trusted\-advisor relationships with customer stakeholders ranging from practitioners and architects to senior IT and business leaders.
- Contributes to CNXN Helix thought leadership and field readiness by staying current on platform capabilities, partner priorities, competitive positioning, and customer adoption trends.
- Mentors junior team members and helps scale field capability through repeatable assets, guidance, and best practices.
Min: USD $138,000\.00/Yr. Max: USD $179,670\.00/Yr. Qualifications:
- Bachelor’s degree in Computer Science, Information Systems, Engineering, Business, or related field, or equivalent practical experience.
- 5\+ years of experience in technical pre\-sales, consulting, solution architecture, customer success engineering, advisory services, or related customer\-facing roles.
- Demonstrated experience leading customer workshops, discovery sessions, strategy sessions, or executive presentations.
- Strong working knowledge of Microsoft 365 Copilot, Copilot Studio, Power Platform, Microsoft Fabric, and Azure services relevant to AI, data, automation, and governance.
- Understanding of AI governance, responsible AI, data governance, security, privacy, identity, compliance, and operational readiness considerations in enterprise environments.
- Practical understanding of Power Platform solution design including Power Apps, Power Automate, Dataverse, environment strategy, governance, ALM, DLP, and Center of Excellence concepts.
- Working knowledge of enterprise AI and developer productivity platforms such as Claude Enterprise, Claude Code, ChatGPT Enterprise, and Codex.
- Ability to translate business requirements into practical solution recommendations, roadmap options, and next\-step engagement plans.
- Strong communication, presentation, facilitation, and stakeholder\-management skills, with the ability to engage both executives and technical teams.
- Ability to create compelling customer\-facing content including workshop materials, recommendations, assessments, and solution positioning.
- Comfortable working across a broad range of industries, customer sizes, and levels of technical maturity.
- Able to balance strategic advisory conversations with practical platform guidance and light demonstration or prototype support where needed.
- Highly collaborative and able to work effectively with account teams, delivery resources, strategic partners, and vendors.
- Strong organizational discipline and ability to manage multiple customer opportunities and priorities simultaneously.
Additional preferred competencies or preferred qualifications:
- Experience with Microsoft\-centric advisory, architecture, or solution consulting in enterprise or mid\-market environments.
- Experience with Microsoft Fabric, Azure AI, Power BI, data platform modernization, or AI governance programs.
- Familiarity with Copilot adoption, agent strategy, enterprise change management, or productivity transformation initiatives.
- Exposure to Dell, HPE, NVIDIA, hybrid infrastructure, or multi\-cloud conversations related to AI workloads.
- Familiarity with workshop\-led selling, proposal shaping, statement\-of\-work support, or consultative solution selling motions.
- Experience in one or more target verticals such as manufacturing, healthcare, retail, financial services, or education.
- Relevant Microsoft certifications such as Azure, Power Platform, Fabric, security, or architecture certifications are preferred.
- Familiarity with enterprise coding assistant deployment and governance patterns is preferred.
Because of the possibility for fraudulent job postings on many popular job boards, please be advised that Connection will never offer a position of employment without a complete interview process and communication with a “live person".
Salary Context
This $138K-$179K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Connection, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($158K) sits 26% below the category median. Disclosed range: $138K to $179K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Connection AI Hiring
Connection has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Merrimack, NH, US. Compensation range: $159K - $179K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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