Interested in this AI/ML Engineer role at Ameriprise Financial?
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About This Role
About Our Company
We’re a diversified financial services leader with more than $1\.5 trillion in assets under management, administration and advisement as of year\-end 2024\. Our team of 22,000 people across 19 countries, serves more than 3\.5 million individual, small business and institutional clients. We are a longstanding leader in financial planning and advice, a global asset manager and an insurer. Our unwavering focus on our clients and strong financial foundation connects each of our unique businesses – Ameriprise Financial, Columbia Threadneedle Investments and RiverSource Insurance and Annuities. Here, we foster meaningful careers, invest in the future, and make a difference for clients, institutions and communities around the world.
Job Description
This role sits at the front line between product teams and the global Sales \& Marketing teams. Role will embed with real users, build working AI prototypes in days, prove what earns adoption, and partner with engineering to productionise the viable ideas.*About the Role*
Columbia Threadneedle is at an inflection point in how it uses AI across its commercial engine. We have the infrastructure, the CRM and commercial data, and world\-class Sales, Marketing and Distribution teams — what we’re building now is the connective tissue between them: people who can sit with a distribution team, understand the problem they’re trying to solve, and build something that works by the end of the week.
This is not a support role. It is not a roadmap role. You will be embedded directly with Sales, Marketing and Distribution teams across North America and EMEA, working on problems that shape how we win and retain clients — and you’ll help drive your solutions from the first conversation to the moment they go live. You show what should ship by testing with users, not by writing decks. The best candidates we’ve spoken to light up when they hear that. If that’s you, read on.
*What You’ll Do*
- Partner directly with Sales, Marketing and Distribution users to turn ambiguous commercial needs into sharply scoped, high\-value problems.
- Rapidly prototype and iterate on LLM\-enabled tools — spanning CRM intelligence, meeting preparation, campaign and audience analytics, and distribution reporting — grounded in real CRM and commercial data.
- Take prototypes from whiteboard to working solution, working largely independently without a detailed specification handed to you.
- Integrate solutions with LLM enablement infrastructure (agent runtimes \& internal data platforms, CRM and marketing analytics platforms) and ensure outputs meet the security, governance and auditability standards of a regulated environment.
- Validate with users through short feedback loops, stopping or redirecting ideas that don’t earn adoption; identify prototypes with production potential and partner with engineering to scale them.
*What Success Looks Like*
In your first year, you’ll have shipped tools that Sales, Marketing and Distribution teams actually use — not prototypes that live in a demo environment, but working solutions embedded in how commercial decisions get made at CTI.
The feedback loop here is immediate. You’re not writing tickets for a product team or waiting six months for a release cycle. You sit with the people who have the problem, you build the solution, and you see it land — or you iterate until it does.
By the end of year one, the measure of success is simple: business teams come to you when they have a problem they think AI can solve. And you’re right often about whether it can — and how.
What We’re Looking For
*Commercial \& Distribution Domain*
- Direct experience working alongside Sales, Marketing or Distribution teams in asset management or financial services — commercial technologists, distribution analytics, or sales/marketing enablement backgrounds are strongly preferred.
- Enough fluency in how funds are sold, serviced and reported (CRM, campaigns, mandates, flows) to contribute to commercial discussions, not just take requirements from them.
*Engineering*
- Strong Python skills to drive production\-quality code, with genuine AWS engineering depth and a bias for rapid prototyping over perfect specifications.
- Hands\-on experience building or deploying agentic AI on Amazon Bedrock AgentCore (the ability to become productive in AgentCore quickly is essential), plus modern software practices — CI/CD, version control and API design.
*The Hybrid*
- A track record of independently taking ambiguous problems from concept to working solution in fast\-moving, loosely defined environments.
- The communication skills to operate fluently on the distribution floor and in the engineering team — in the same day — applying security, data\-governance and responsible\-AI guardrails with appropriate evaluations and human oversight.
Base Pay Salary
The estimated base salary for this role is $145,000 \- $175,000 / year. We have a pay\-for\-performance compensation philosophy. Your initial total compensation may vary based on job\-related knowledge, skills, experience, and geographical work location. In addition, most of our roles are eligible for variable pay in the form of bonus, commissions, and/or long\-term incentives depending on the role. We also have a competitive and comprehensive benefits program that supports all aspects of your health and well\-being, including but not limited to vacation time, sick time, 401(k), and health, dental and life insurance.Full\-Time/Part\-Time
Full timeExempt/Non\-Exempt
ExemptJob Family Group
TechnologyLine of Business
TECH Technology*Ameriprise Financial is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, national origin, ancestry, age, physical or mental disability, medical condition, pregnancy, military status, veteran status, genetic information, citizenship, disability status, marital status, family status or any other basis prohibited by law.*
*We are committed to fostering an inclusive and accessible recruitment process for individuals with disabilities. If you require a reasonable accommodation to participate in the application or interview process, speak to your recruiter to discuss how we can support you.*
Salary Context
This $145K-$175K range is below the median 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 Ameriprise Financial, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($160K) sits 26% below the category median. Disclosed range: $145K to $175K.
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.
Ameriprise Financial AI Hiring
Ameriprise Financial has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $175K - $175K.
Location Context
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
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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