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About This Role
Synergy Technical is looking for an AI & Data Solutions Strategist who combines deep
technical expertise with the ability to lead customer conversations, architect solutions, tell
a compelling technical story, and turn ideas into solutions customers want to buy.
This isn't a strategy-only role, and it isn't traditional presales.
You'll work directly with customers to understand what they're trying to accomplish,
architect solutions, lead technical discussions, build demos, validate scopes and
estimates, and help turn opportunities into revenue.
You'll bring deep expertise in Microsoft Fabric and Power BI, strong working knowledge of
Power Platform and the broader Microsoft ecosystem, and strong AI fluency with the ability
and curiosity to work across platforms including Microsoft Copilot, Claude and Gemini.
This role requires someone who thrives in high-pressure, customer-facing situations. You'll
sometimes need to think on your feet, navigate difficult questions, adjust when a
conversation takes an unexpected turn, and confidently lead the customer toward a path
forward—even when you don't immediately have every answer.
Most importantly, you'll be expected to operate as a technical authority during the sales
process. You should be able to independently lead technical discovery, design and validate
solutions, deliver demonstrations, and handle substantive technical questions without
routinely requiring Delivery resources for technical validation or support.
This is a quota-carrying, commission-eligible role. You won't simply support
opportunities—you'll help create, shape and win them.
You Might Be a Great Fit If...
You may have started your career as an engineer, architect, consultant or developer—but
somewhere along the way, you realized you enjoy being in front of customers as much as
you enjoy building technology.
You like explaining complicated things. You like whiteboards. You like demos. And you're at
your best when the stakes are high.
You aren't rattled when a customer challenges an assumption, a demo doesn't go exactly
as planned, or someone asks a question you weren't expecting. You can stay composed,
think on your feet and keep the conversation moving.
You don't need to know every answer. But you're comfortable saying, "I don't know yet,"
asking the right questions, and figuring out what comes next.
When new technology changes how the job gets done, your instinct isn't to protect the old
way of working. You want to learn it, test it and figure out what it makes possible.
You'll use AI extensively in this role. We don't need you spending hours writing a SOW that
an AI agent can draft in minutes.
We need you to know whether that SOW is right.
You want to be in the room. You want to help shape the solution. You want to win the work.
Key Responsibilities
- Lead technical discovery sessions, workshops and solution discussions with
technical and executive stakeholders.
- Architect AI, data and analytics solutions using Microsoft Fabric, Power BI, Azure
AI, Copilot, Power Platform and other appropriate technologies.
- Evaluate and incorporate platforms such as Claude, Gemini and emerging AI
technologies when they can create value for customers.
- Use Synergy Technical's AI agents to accelerate SOW development, solution
design, research and estimation while independently validating architecture,
scope, assumptions, effort, dependencies, feasibility and risk.
- Build and independently deliver technical demos, prototypes and presentations
that help customers understand what's possible.
- Navigate challenging customer conversations, technical objections and
unexpected questions with confidence and composure.
- Develop and independently deliver webinars, workshops and technical briefings
that educate customers and generate opportunities.
- Maintain strong working knowledge of Power Platform capabilities and recognize
when Power Apps, Power Automate, Copilot Studio and related technologies
should be incorporated into a solution.
- Identify new opportunities and partner with Sales from discovery through
proposal and close.
- Serve as the primary technical authority for assigned opportunities, engaging
Delivery resources when specialized expertise is required rather than relying on
Delivery for routine technical validation.
- Translate complex technologies into business outcomes for both technical and
executive audiences.
- Continuously evaluate, test and learn emerging AI, data and automation
technologies and determine how they can create value for our customers.
Experience & Qualifications
Required
- Ability and willingness to travel to customer, partner and company locations
multiple times throughout the year for customer meetings, workshops,
presentations, partner engagements, conferences and other business events.
- 5+ years of experience in solution architecture, consulting, engineering,
development, technical sales, data/analytics or a related technical role.
- Demonstrated hands-on expertise with Microsoft Fabric and Power BI, including
data modeling, analytics and modern data architecture.
- Hands-on experience designing or implementing AI, data or analytics solutions
in enterprise environments.
- Strong working knowledge of Microsoft Power Platform and its role within
broader business solutions.
- Experience leading technical discovery sessions, architecture discussions and
customer workshops.
- Demonstrated ability to perform effectively under pressure, remain composed in
challenging customer situations, and think on your feet when faced with
unexpected technical questions or changing circumstances.
- Ability to independently translate business requirements into technically sound
architectures, scopes and estimates.
- Ability to independently evaluate solution architecture, technical feasibility, level
of effort, dependencies and delivery risk.
- Demonstrated ability to build and deliver technical demonstrations and
presentations without routinely requiring additional technical support.
- Ability to communicate complex technical concepts to both technical
audiences and business executives.
- Experience working with generative AI and at least one major enterprise AI
platform.
- Demonstrated ability to rapidly learn, test and apply new technologies.
- Strong commercial instincts and comfort working in a revenue-focused, quota
carrying role.
Preferred
- Experience working across multiple AI platforms, particularly Microsoft Copilot,
Claude and Gemini.
- Experience with Copilot Studio, Azure AI services, AI agents or agentic
architectures.
- Experience designing solutions that combine enterprise data with AI.
- Experience delivering webinars, conference presentations, workshops or other
technical thought leadership.
- Previous experience in consulting, professional services or a Microsoft partner
environment.
- Experience supporting or directly contributing to the sale of professional
services and technology solutions.
Certification Requirements
Candidates must hold at least one current technical certification in artificial intelligence
from Microsoft or another recognized enterprise technology provider.
The certification should demonstrate foundational or advanced knowledge of AI
technologies, generative AI, AI solution development or enterprise AI implementation.
Microsoft certifications aligned with Azure AI, Microsoft Fabric, Power BI/Data Analytics or
Azure Solutions Architecture are strongly preferred in addition to the required AI
certification.
Additional certifications or demonstrated technical proficiency with Microsoft Copilot,
Claude, Gemini, Power Platform or other AI technologies are highly valued.
Because AI technologies are evolving rapidly, continuous technical development is an
essential expectation of this role. The successful candidate will be expected to maintain
relevant certifications and continually develop hands-on proficiency with emerging AI, data
and automation technologies.
How We'll Measure Success
Success means:
- Creating and influencing revenue and generating new opportunities.
- Helping convert technical conversations into closed business.
- Designing technically sound solutions that Delivery can confidently execute.
- Independently leading customer technical conversations, demonstrations,
webinars and presentations.
- Reducing the routine need for billable Delivery resources during the sales
process.
- Expanding Synergy Technical's AI, data and analytics practice.
- Staying ahead of rapidly changing technology and turning what you learn into
customer and business value.
It also means being someone the team can confidently put in front of a customer when the
conversation is complex, high stakes or unpredictable. You can lead the room, adapt when
things don't go according to plan, and maintain technical credibility and composure when
challenged.
What This Role Isn't
This isn't a traditional presales role where you wait for Sales to tell you which meeting to
attend.
It isn't a strategy role where you talk about technology without being able to get into the
technical details.
And it isn't a role where Delivery should routinely need to provide the technical expertise
behind your SOWs, demonstrations or customer conversations.
We're looking for someone who wants to be part architect, part engineer, part teacher,
part strategist and part seller—and who gets excited about figuring out what's possible
Salary Context
This $135K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Synergy Technical, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($142K) sits 33% below the category median. Disclosed range: $135K to $150K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Synergy Technical AI Hiring
Synergy Technical has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Richmond, VA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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