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Who We Are
Escalent is an award\-winning data analytics and advisory firm that helps clients understand human and market behaviors to navigate disruption. As catalysts of progress for more than 40 years, our strategies guide the world’s leading brands. We accelerate growth by creating a seamless flow between primary, secondary, syndicated, and internal business data, providing consulting and advisory services from insights through implementation. Based on a profound understanding of what drives human beings and markets, we identify actions that build brands, enhance customer experiences, inspire product innovation, and boost business productivity. We listen, learn, question, discover, innovate, and deliver—for each other and our clients—to make the world work better for people.
Role Overview
This role sits within our AI and Innovation Hub — an enterprise\-wide team focused on operationalizing and integrating AI tools and capabilities into our core business of research \& consulting, as well as our corporate function workflows. Ultimately, this role actively contributes to our efforts to develop in\-house and monetizable AI products and solutions to foster better, smarter, and efficient workflows.
As a research and advisory firm, the ideal candidate is an experienced professional with notable Strategy Research and/or Consulting experience, exposure to Workflow and Product Development, high degree of AI\-and\-tech savviness, and a Consultative and Impact oriented mindset.
Responsibilities
- Develop a strong understanding of Escalent group's products, tools, solutions, and technological roadmap and use as context for AI initiatives related to company workflows and products.
- Demonstrate a strong capability to link findings and recommendations to desired business impact.
- Present findings and recommendations, while explaining approach and rationale, to various business teams as and when needed.
- Contribute to Escalent's ongoing and ever\-expanding AI upskilling initiatives by conceptualizing ideas, developing collateral, and administering sessions.
- Organize and administer demos, show\-n\-tells, and lunch\-n\-learns among internal/external audiences to socialize the product/feature/agent, resolve queries, foster adoption, and gather constructive and actionable feedback.
- Exhibit a strong passion for AI and innovation, and stay updated on the latest developments, trends, product/model launches, etc. and their plausible impact on Escalent's AI strategy /approach/roadmap.
- Address internal queries on AI, innovation, and automation tools/techniques/approaches to ensure teams know when (and when not) to use AI, how to use it effectively \+ efficiently \+ ethically, which tool/feature/product/agent to leverage, and where to avoid pitfalls.
- 3P Toolkit Due Diligence and Expansion
+ Identify relevant AI/innovation tools for any use case, via desk research and internal outreach.
+ Liaise with various tool vendors to understand functional possibilities, evaluate use case fitment, and assess commercial viability.
+ Partner with Compliance and IT teams to vet and whitelist specific tools for test/trials.
+ Conceptualize a trial program and secure licenses for testing.
+ Partner with domain/functional/corporate teams to test various tools to assess their effectiveness and value\-add.
- Internal and SharePoint Documentation
+ Prepare, maintain, and improve documents on AI\-related processes, guidelines, and protocols.
+ Author self\-learn collateral on the in\-house and approved 3P tool mix, such as tool one\-pagers, tool usage decision flowcharts, SharePoint pages and content, etc.
Support AI Product Development
- Where applicable, co\-develop the development/implementation and scaling plan, in collaboration with the tech, development, and product teams.
- Where applicable/possible, adopt a hands\-on approach on low\-code/no\-code/vibe\-code agent development tools/environments to create the tool/agent.
- Craft and co\-implement alpha and beta testing plans, and internal and external rollout roadmaps, to ensure controlled testing, scaled launch, robust adoption, and continuous improvement.
- Double up as an alpha tester focused on 'breaking' the product/feature/agent to ensure adequate stress\-testing under various edge cases.
Qualifications
- 5\-7 years in delivering Market Research \& Insights engagements across industries, focused on international markets.
- Bachelor's degree from an accredited university, or equivalent combination of education and experience
- Must Have: hands\-on experience with AI and innovative tools \+ ability to quickly ramp\-up on new ones.
- Big Plus: experience with low\-code/no\-code/vibe\-code agent development and working with tech/development/product teams.
- Solid experience leading project\-based engagements and delivering findings to client and internal stakeholders.
- Strong exposure to internal process innovation/automation.
- Some experience in cross\-functional, enterprise\-wide initiatives.
- Excellent communication and presentation skills
- Collaborative leader with strong interpersonal skills, comfortable coaching and reviewing the work of junior colleagues
- Logical and creative thinker with astute analytical aptitude
- Consultative approach to problem solving
- Strong attention to detail
Work Environment \& Compensation
- Location: Remote – Ontario, Canada
- Canada Salary Range: CAD $100,000 – $125,000\. Competitive, market\-aligned compensation commensurate with experience
- Travel: Minimal, based on project needs
Explore our Careers and Culture page to learn more about the people behind the brand: https://escalent.co/careers\-and\-culture/
Salary Context
This $100K-$125K range is in the lower quartile 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 Escalent, 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 in Demand for This Role
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 ($112K) sits 48% below the category median. Disclosed range: $100K to $125K.
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.
Escalent AI Hiring
Escalent has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $125K - $125K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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