Interested in this AI/ML Engineer role at Columbus Sverige?
Apply Now →Skills & Technologies
About This Role
Role Summary
We are seeking a Functional Solution Architect – AI for Microsoft Business Applications \& Industry Verticals to drive AI\-enabled business transformation across Finance, Supply Chain, Manufacturing, Life Sciences, and Robotics/Industrial Automation. This is a highly client\-facing role with strong emphasis on pre\-sales, solutioning, and executive demos.
The ideal candidate can confidently demonstrate out\-of\-the\-box D365 F\&O and CE agents, Agent 365, and Copilot experiences, shape solution designs that include MCP (Model Context Protocol) server deployments, and translate complex business problems into AI\-powered process solutions that clients can operationalize.
Key Responsibilities
Pre\-Sales, Demos \& Client Engagement
- Lead client discovery sessions, solutioning workshops, and executive briefings focused on AI for BizApps and industry\-specific processes.
- Design and deliver high\-impact demos showcasing:
+ Default (OOTB) D365 F\&O and CE agents
+ Agent 365 capabilities
+ Copilot experiences embedded in Dynamics 365 and Power Platform
+ End\-to\-end AI scenarios across Finance, Supply Chain, Manufacturing, Life Sciences, and Robotics
- Partner with sales teams to create AI solution narratives, proposals, value propositions, and business cases.
- Support RFPs/RFIs, PoC design, and technical\-functional solution storytelling for AI\-driven transformation programs.
Functional Architecture \& Business Process Design
- Lead functional architecture for AI\-enabled business processes across:
+ Finance: R2R, P2P, O2C, FP\&A, close and compliance automation
+ Supply Chain: demand planning, inventory optimization, logistics, supplier collaboration
+ Manufacturing: production planning, quality management, asset maintenance, MES integration
+ Life Sciences: quality, regulatory documentation, batch traceability, GxP processes
+ Robotics \& Industrial Automation: IoT\-enabled operations, predictive maintenance, digital twins
- Map current\-state processes and define future\-state AI\-augmented workflows leveraging D365 F\&O/CE and non\-Microsoft platforms.
- Define functional requirements for AI agents, copilots, and intelligent workflows embedded into daily operations.
AI Agents, MCP Server Design \& Solutioning
- Drive functional design and use\-case definition for:
+ OOTB D365 F\&O and CE agents
+ Agent 365
+ Copilot Studio and custom business process agents
- Shape solution designs that include MCP server deployments to securely connect AI agents with enterprise systems, tools, and data sources.
- Collaborate with technical architects to:
+ Define MCP server architecture, deployment patterns, and integration touchpoints
+ Ensure MCP servers support secure tool access, data grounding, and agent orchestration across BizApps and non\-Microsoft platforms
- Contribute to design decisions around agent governance, context management, security boundaries, and operational support models for MCP\-based architectures.
Advisory, Transformation \& Change Leadership
- Act as a trusted functional AI advisor to business leaders and process owners.
- Lead AI transformation discussions, operating model design, and value realization planning.
- Support organizational change management and adoption strategies for AI agents and copilots.
- Contribute to Responsible AI adoption from a business and operating model perspective (governance, controls, user enablement).
Required Qualifications
- 10\+ years of experience in functional consulting, business process transformation, or enterprise applications
- Deep functional expertise in two or more of the following:
+ Finance
+ Supply Chain
+ Manufacturing
+ Life Sciences
+ Robotics / Industrial Automation
- Strong experience with Microsoft Dynamics 365 F\&O and/or CE
- Proven experience leading client demos, pre\-sales workshops, and solutioning engagements
- Hands\-on familiarity with:
+ Out\-of\-the\-box D365 F\&O and CE agents
+ Agent 365 and Copilot experiences
+ MCP server concepts and deployment patterns (or equivalent agent integration frameworks)
- Ability to collaborate with technical teams on MCP server architecture and operational support models
Preferred Qualifications
- Consulting or SI background with AI\-focused pre\-sales leadership
- Experience designing or supporting agent orchestration platforms
- Familiarity with RAG pipelines, vector search, and enterprise AI grounding patterns
- Industry experience in Manufacturing, Life Sciences, or Industrial Automation
- Microsoft certifications in Dynamics 365 Finance, Supply Chain, or Industry Solutions
What You’ll Deliver
- Client\-ready AI demos and solution blueprints including MCP server\-based architectures
- Industry\-specific AI agent use case catalogs for BizApps
- Functional designs for AI\-enabled future\-state processes
- Business cases that link AI adoption to measurable operational outcomes
Full Time Benefits:
- Columbus offers a competitive benefits package to all full\-time employees. This package includes Health, Life, Vision and Dental Insurance, Short\- and Long\- Term Disability, in addition to, paid vacation, sick leave, holidays and 401(k).
Why join Columbus?
People always come first at Columbus. We’re a global digital consultancy with a local presence, helping businesses transform and thrive through technology, data, and human insight. Just as importantly, we’re a workplace where careers are nurtured and development is supported through clear, structured career paths.
Our culture is built on trust, collaboration, curiosity, and a shared commitment to delivering customer success. Whether you're an experienced professional or just starting out, you’ll find the freedom to explore ideas, challenge convention, and shape your own path.
With over 1,500 colleagues across more than 10 countries, we bring global perspectives and local understanding. What unites us is our belief in creating meaningful impact — for our people, our customers, and the journey ahead.
Let’s thrive, grow, and shape the future together.
Disclaimer: The use of any AI Tools or assistance during the interview process is not permitted.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Columbus Sverige, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Columbus Sverige AI Hiring
Columbus Sverige has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.