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About This Role
Job Description
We are looking for an AI Enablement Engineer who can help enterprises adopt Gemini Enterprise and turn Generative AI capabilities into measurable business value. The role combines AI solution engineering, customer enablement, and business problem-solving, working closely with customers to identify high-impact AI opportunities, rapidly prototype solutions, and drive adoption. You will act as a bridge between AI capabilities, business workflows, and technical implementation, helping customers move from identifying an opportunity to building, demonstrating, and scaling practical AI solutions
Responsibilities:
- + Key Responsibilities:
- Work with enterprise customers to understand business processes, pain points, and opportunities where AI can deliver measurable value.
- Conduct AI discovery and Value Finder workshops to identify and prioritize high-impact AI use cases.
- Rapidly prototype and demonstrate solutions using Gemini, Vertex AI, LLMs, RAG, AI Agents, APIs, and enterprise data.
- Build AI-powered workflows, assistants, agents, and automation solutions based on customer requirements.
- Integrate AI solutions with enterprise applications, APIs, databases, knowledge sources, and business systems.
- Work with authentication, connectors, data pipelines, and APIs to extend AI capabilities beyond out-of-the-box functionality.
- Partner with AI Engineers and Solution Architects to move successful prototypes toward production.
- Conduct customer demos, workshops, enablement sessions, and hands-on training.
- Create reusable AI agents, workflows, prompts, playbooks, and enablement assets.
- Track AI adoption and business outcomes such as usage, productivity, time savings, automation, and ROI.
- Present technical solutions, business outcomes, and recommendations to business and technology stakeholders.
- Identify opportunities to scale successful AI solutions across teams and business functions.
Top Non-Negotiable Skills:
- + - Experience in AI Solution Engineering, Customer Engineering, AI Enablement, AI Consulting, or similar customer-facing roles, preferably working with enterprise customers.
- Proven ability to lead customer discovery sessions, Value Finder workshops, AI enablement programs, and adoption initiatives that translate business challenges into high-value AI use cases.
- Strong ability to understand business workflows and simplify Generative AI and Agentic AI concepts for business users and executive stakeholders.
- Hands-on experience with Generative AI platforms such as Google Gemini Enterprise, Vertex AI, Microsoft Copilot, ChatGPT Enterprise, Claude Enterprise, or similar platforms.
- Experience building or working with APIs, custom connectors, and enterprise integrations to extend AI capabilities beyond out-of-the-box functionality.
- Ability to work with enterprise applications, business data sources, authentication, and data pipelines while partnering with engineering teams on production implementation.
- Demonstrated ability to define and quantify business value and ROI, including productivity gains, time savings, automation, adoption, and operational improvements.
- Strong customer-facing communication, workshop facilitation, presentation, and stakeholder management skills, with confidence working with CXO and senior leadership teams.
- Basic understanding of cloud environments and enterprise application architecture, including how AI solutions are deployed, integrated, and managed.
- Strong problem-solving, analytical thinking, and Data Structures & Algorithms fundamentals.
Negotiable Skills:
- + Familiarity with Gemini Enterprise solutions is a plus. + Experience with Cloud Fundamentals for deployment is beneficial.
About Techolution:
Techolution is the world’s leading FEA – Full Enterprise Automation specialist. We are trusted by boards and executive leaders who need AI with real business outcomes. Despite Gen AI, LLMs, and billions in investment, most enterprise AI programs aren’t delivering results. Techolution fixes that with BPA 4.0. We take the power of big Tech’s Generic AI and multiply it with our Expert AI stack — resulting in business process automations that follow the same nuances as domain experts do on the job at an unprecedented speed. Our platform has delivered over $900M in AI ROI for our clients in less than a year, all while increasing their AI Adoption by 10x.
What We Do
As your one-stop shop engineering firm, Techolution guides you from ideation to ROI realization. We ensure real-world AI success by minimizing R&D risks for our clients with our innovative fixed-bid pricing model.
Our 3 Power Moves for ROI from AI
- Value Finder: We identify high-impact AI and automation opportunities in enterprises and build solutions that deliver measurable value in as little as 30 days.
- 100K Challenge: Work on fast-paced AI initiatives where teams build solutions designed to generate 2× ROI within 100 days.
- GE LEAP: Contribute to enterprise AI transformation through our partnership with Google, helping scale Gemini-driven solutions across organizations.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Techolution, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Techolution AI Hiring
Techolution has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Ridgewood, NJ, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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