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About This Role
Sales Specialist – Applications & AI
At Ensono, our Purpose is to be a relentless ally, disrupting the status quo and unleashing our clients to Do Great Things! We enable our clients to achieve key business outcomes that reshape how our world runs. As an expert technology adviser and managed service provider with cross-platform certifications, Ensono empowers our clients to keep up with continuous change and embrace innovation.
We can Do Great Things because we have great Associates. The Ensono Core Values unify our diverse talents and are woven into how we do business. These five traits are the key to achieving our purpose:
Honesty, Reliability, Curiosity, Collaboration, and Passion.
About the role and what you’ll be doing:
The Sales Specialist – Applications & AI is a quota-carrying, commissioned sales role responsible for growing Ensono’s Application Managed Services (AMS) and AI business. You will sell outcomes across the full stack, from the business processes our clients depend on, through the applications that run them, down to the data, AI and infrastructure underneath. Your goal is to help enterprise clients run their critical applications more reliably, change them faster, and put AI into production where it delivers measurable value.
You collaborate with Account Executives and Client Partners who own the primary account relationship across Ensono’s client base while bringing expertise and specialized selling on Applications & AI to build and close your own pipeline. You will bring the application and AI expertise, the executive point of view, and leadership that turn a client problem into a signed services agreement.
Ensono is an AI-led company, leveraging AI in its own operations and in live, mission-critical client environments across mainframe, hybrid, cloud, and applications, so you will be selling capabilities that already run in production, not just in a pilot.
Sales Strategy & Opportunity Development
- Own and deliver quarterly and annual bookings targets for Application Managed Services and AI, across both overlay opportunities and the pipeline you source yourself.
- Prospect for and open new application and AI conversations with C-level leaders, application leaders and business unit leaders, including in accounts where Ensono does not yet manage applications or AI.
- Partner with Account Executives and Client Partners on account plans that expand Ensono’s role from infrastructure into the application, data and AI layers.
- Lead deal strategy from first conversation through contract: lead with a business problem, qualify rigorously, shape the solution, build the business case and drive to close.
- Use focused entry engagements, such as application observability and insight reviews, AI-assisted application and code assessments, data-readiness-for-AI assessments, AI strategy workshops and time-boxed AI proofs of value, to create early wins that convert into recurring managed services.
- Forecast accurately, keep CRM current, and give leadership a clear view of pipeline health, risks and upside.
- Maintain an informed view of client priorities, competitive activity, and the fast-moving AI and application services market.
Client Engagement & Solution Leadership
- Act as a trusted adviser on how to run, modernize and improve critical applications across mainframe, cloud and hybrid estates, and how AI can make them faster, more reliable and less costly to operate.
- Connect technology to business outcomes, translating faster change delivery, fewer incidents, lower run cost, reduced risk and new AI-enabled capabilities into terms that CIOs, CFOs and business leaders will fund.
- Facilitate discovery conversations, workshops and assessments with technical, operational and executive stakeholders to understand business challenges, identify the outcomes that matter most, and prioritize where application and AI investments will pay back first.
- Drive client conversations by leading with the client’s business outcomes, particularly around AI, working with business leaders to understand what they want to deliver on and work with the internal Ensono team to design a solution that meets that business objective.
- Position AI-enabled operations, including predictive incident detection, AI-driven root cause analysis, intelligent change-risk scoring and automated remediation, as part of how Ensono runs applications rather than as an add-on.
- Position Ensono’s AI Managed Services and connect opportunities to build out a client’s AI capabilities with how they plan to manage/govern those capabilities, connecting consulting (non-recurring revenue) to ongoing services for Ensono (recurring revenue opportunities).
- Build business cases, ROI models and commercial proposals, including SLA- and outcome-based structures, that support confident executive decisions.
- Address responsible AI, security, data governance and regulatory considerations credibly with clients in highly regulated industries.
Collaboration & Field Execution
- Orchestrate Solution Architects, AI and data specialists, application delivery leaders and partners to design solutions that can be delivered and run profitably.
- Lead solution positioning, proposal development, pricing discussions, presentations and contract negotiations for application and AI opportunities.
- Work with hyperscaler and AI technology partners on joint account planning, co-selling and partner-funded programs.
- Raise application and AI fluency across the sales organization by running enablement, sharing winning plays and mentoring sellers on how to find and qualify opportunities.
- Use AI in your own selling, including account research, meeting preparation, proposal development and pipeline analysis, and share what works with the team.
- Partner with marketing and portfolio teams on campaigns and programs that build application and AI pipeline.
Business Insights & Voice of the Customer
- Bring client feedback, competitive intelligence and win/loss insights back to portfolio, product and AI teams to shape offers, packaging and pricing.
- Actively identify barriers to customer success and help develop strategies that improve client outcomes.
- Advocate for client needs while balancing business objectives and operational realities.
- Support strategic growth initiatives and other responsibilities as assigned.
We want all new Associates to succeed in their roles at Ensono. That’s why we’ve outlined the job requirements below. To be considered for this role, it’s important that you meet all Required Qualifications. If you do not meet all of the Preferred Qualifications, we still encourage you to apply.
What You Will Need:
- Bachelor’s degree from a four-year college or university or equivalent work experience.
- Experience within a managed services provider (MSP), consulting organization, systems integrator, or technology services company.
- 7+ years of quota-carrying enterprise technology sales experience, including at least 3 years selling application services such as application managed services, application modernization, application development and support, or DevOps/SRE services.
- A consistent record of meeting or exceeding quota on complex, multi-year services deals, including opportunities you sourced yourself.
- Direct experience selling AI, data or intelligent automation solutions (for example, generative or agentic AI use cases, AIOps, or the data platforms that support AI), and the ability to explain what made those deals succeed beyond the pilot stage.
- Working knowledge of how modern applications are built, run and supported, including the Software Development Lifecycle (SDLC), Agile, CI/CD, ITIL-based service management, observability and SRE practices, with enough depth to hold credible conversations with application and engineering leaders.
- Familiarity with cloud platforms including AWS, Microsoft Azure, and Google Cloud.
- Proven ability to engage and influence senior executives, including CIOs, CTOs, CFOs and business unit leaders, and to build business cases that tie technology investment to financial and operational outcomes.
- Experience in a sales specialist, overlay, or solution sales role, with a proven ability to influence without direct authority across a matrixed organization.
- Strong verbal, written, presentation and executive communication skills, with strong analytical and problem-solving ability.
- Personal fluency with AI tools in your day-to-day work, and the curiosity to keep pace with a fast-moving market.
- Proficiency with CRM tools, Microsoft Office applications, and sales enablement platforms.
Bonus Qualifications
- Experience selling application services or modernization for mainframe or other legacy platforms alongside cloud.
- Experience selling AIOps, observability or AI-enabled IT operations services.
- Familiarity with emerging AI engineering practices such as specification-driven development, AI agent orchestration and workflow design, and Model Context Protocol (MCP) server architectures.
- Familiarity with data governance, data mesh, data warehousing and data virtualization, and the data foundations AI depends on.
- Experience structuring SLA-, XLA- or outcome-based commercial models.
- Experience selling into regulated industries such as financial services, insurance, healthcare or the public sector, or deep expertise in another industry vertical aligned to Ensono’s growth strategy.
- Experience working with large enterprise and mid-market clients and participating in large, strategic, multi-service transformation programs.
- Relevant certifications in cloud, AI, application or managed services technologies.
Why Ensono?
Ensono is a place to make better happen – for our clients and for your career. You can do great things through innovation or collaboration, by learning or volunteering, or to promote diversity and inclusion. You can do great things for your own health or for a healthier planet. Whatever it means to you to do great things we want Ensono to be the place you can do it.
We are a client-facing business, but we do encourage clients to allow us to work remotely most of the time so if you are not required to be on a client site, you can choose to work from home or in our Ensono offices.
Some of our benefits include:
- Unlimited Paid Days Off
- Three health plan options
- 401k with company match
- Eligibility for dental, vision, short and long-term disability, life and AD&D coverage, and flexible spending accounts
- Family Forming Benefit including fertility coverage and adoption/surrogacy reimbursement
- Paid childbearing and paternal leave
- Education Reimbursement, Student Loan Assistance or 529 College Funding
- Sabbatical leave
- Wellness program
- Flexible work schedule
As of the date of this posting, a good faith estimate of the current pay scale for this role is $144,000 to $188,000 annually based on a full-time schedule. Please note that placement in the range may vary based on numerous factors including but not limited to skills, experience, internal equity, and business needs. In addition to base salary, other compensation programs, depending on eligibility, includea role-based, sales-incentive plan, and an equity grant under our Associate Equity Appreciation Program.
Ensono is an Equal Opportunity/Affirmative Action employer. We are committed to providing equal employment to our Associates and building a diverse and inclusive workforce. All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or other legally protected basis, in accordance with applicable law.
Pay transparency nondiscrimination statement/posting OFCCP’s pay transparency policy can be found on OFCCP’s website.
If you need accommodation at any point during the application or interview process, please let your recruiter know or email [email protected].
Salary Context
This $144K-$188K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ensono, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($166K) sits 22% below the category median. Disclosed range: $144K to $188K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Ensono AI Hiring
Ensono has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $188K - $188K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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