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About This Role
HYBRID - Montreal, Canada What is Equisoft? Equisoft is a global provider of digital solutions for insurance and investment, recognized by over 250 of the world's leading financial institutions. We offer a comprehensive ecosystem of scalable solutions that help our customers meet all the challenges brought about by this era of digital transformation, thanks to our business needs-driven approach, industry knowledge, cutting-edge technologies and experts. With its business-driven approach, in-depth industry knowledge, cutting-edge technologies and multicultural team of experts based in North America, the Caribbean, Latin America, Europe, Africa, Asia and Australia, Equisoft helps its customers meet the challenges of this era of digital transformation.
Why Choose Equisoft? With 950+ employees, we are a stable organization that offers career advancement opportunities and fosters a stimulant environment. If that’s not enough, then check out these other perks below:
- Hiring Location: Canada / USA (Eastern Time Zone)
- Internal position title: Principal, AI for Services Delivery
- Full-time Permanent Role
- Benefits available day 1: Medical, Dental, Retirement Plan, Telemedicine Program, Employee Assistance Program, etc.
- Number of hours per week: 40
Educational Support (LinkedIn Learning, LOMA Courses and Equisoft University) *
Role: The Principal Strategist, AI for Services Delivery reports to the Chief Operating Officer. Equisoft implements different solutions for large and complex life and annuity carriers worldwide. This role owns how AI is used across the full implementation lifecycle: requirements analysis, use case definition, configuration, integration, and testing. It is central to Equisoft’s objective of maximizing the use of AI across the enterprise, giving delivery teams the tools to accelerate implementations, with a target of 25%+ effort reduction across projects.
Your Day with Equisoft:
- Set the approach for using AI in requirements analysis, use case definition, OIPA rules and configuration, integration, and test case generation
- Take the AI ideas Equisoft has already identified and turn them into tools, techniques and playbooks ready to roll out across our services and regions
- Look for new ways to innovate with AI in implementations, especially around configuration, in a cost-effective way
- Build a governance framework: which AI tools are approved, how outputs get reviewed, and where human sign-off is mandatory
- Drive the 25%+ effort reduction target on Equisoft/manage and Carbon implementations, and track progress project by project
- Develop reusable AI-assisted workflows for generating OIPA rules and complex SQL, drafting user stories, and automating test case creation
- Select and/or develop the tools that support delivery, including MCP integrations with Atlassian and Azure DevOps, and turn them into practical delivery playbooks
- Keep the practice current as models and tools change: retire what doesn’t work, scale what does
- Deploy the standards and playbooks across every delivery region: USA, Canada, Caribbean, LATAM, EMEA/APAC, and the India Global Capability Centre (configuration, QA, data migration)
- Train delivery leads and practitioners on approved tools and workflows
- Adapt the rollout to each region’s maturity and workload rather than applying one plan everywhere
- Audit project teams against the defined AI standards, and flag and correct deviations
- Report compliance and effort-reduction metrics to delivery and executive leadership
- Own data handling and IP risk for AI use on carrier-confidential project data, working with Information Security
Work with regional delivery VPs to align the rollout with each region’s delivery calendar, and with the enterprise AI and IT teams to stay aligned on direction *
Requirements
Technical
- 10+ years in enterprise software delivery, with meaningful time developing systems using AI
- Direct experience across the phases this role covers: requirements analysis, use case definition, configuration, integration, and testing
- A track record of introducing AI or automation into a delivery organization, not just piloting it
- Experience leading initiatives across distributed, multi-region teams
Soft skills
- Strong sense of organization and prioritizing
- Ability to influence and drive adoption across regions without direct reports
- Analytical and problem-solving skills
- Ability to communicate, write and synthesize information, and to train and coach practitioners
Team spirit, tact, diplomacy, autonomy, rigor, and discipline *
Nice to haves:
- Life and annuity insurance domain expertise
- Experience with AI tooling in software delivery: LLM-assisted code and rule generation, automated test generation, MCP-based integrations
Experience standing up AI governance frameworks in regulated or carrier-confidential environments *
Equisoft is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
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 EQUISOFT, 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. Senior-level AI roles across all categories have a median of $229,300.
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.
EQUISOFT AI Hiring
EQUISOFT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Philadelphia, PA, US.
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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