Interested in this AI/ML Engineer role at Campana & Schott Business Services GmbH?
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About This Role
Campana \& Schott Inc. is the New York City\-based innovation hub of the Campana \& Schott Group, a privately held, 30\-year\-old management and technology consulting firm with over 600 employees across Europe and the US. We create tangible results for our clients by designing digital solutions including business transformation, digital workplace, customer insights and analytics and creating novel ways of working. In the U.S., Campana \& Schott focuses on healthcare and pharmaceuticals, combining digital and technology innovation with change management and transformation to create new momentum.
We are seeking a full\-time AI Solutions Analyst to join a small, fast\-moving team in New York. The core of this role is building \- developing backend services, AI\-powered applications, and internal tools using Python and cloud infrastructure. You'll also contribute to consulting engagements, so strong communication skills matter just as much as technical chops.
Our Values
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Passion
We approach every single project with enthusiasm – we thrive on new challenges and taking the perspective of our clients and their people.
Diversity
We value the diversity of the people in our company – irrespective of nationality, family situation, sexual orientation, religion, or age. Our diversity benefits us all – we want to see things from your perspective!
Respect
We treat each other with respect and as equals – regardless of our age, position in the hierarchy, or role in the company. We’re all on first name terms and are always available for our colleagues.
Day in the Life
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Build
- Develop and maintain backend services, scripts, and automations using Python and related frameworks
- Design and implement AI\-powered solutions, including LLM integrations, prompt engineering, and agentic workflows
- Work across the stack as needed \- APIs, databases, cloud infrastructure, and frontend interfaces
- Write tests, maintain documentation, and ensure production reliability
Collaborate
- Help translate business problems into technical solutions and communicate trade\-offs effectively
- Contribute to client\-facing engagements when needed, including research, analysis, and deliverable creation
- Communicate technical concepts and recommendations clearly to both technical and non\-technical audiences
What's in it for You
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Join a dynamic team with a Start\-Up atmosphere supported by strong roots of 30 years of successful business in Europe. If you are looking for a fast\-paced working environment with exciting and innovative mandates around business transformation and if you are enjoying working closely with great colleagues and great clients, then this is a unique opportunity for you.
Career Development
Benefit from joining our fast\-growing U.S. team, take over entrepreneurial and leadership responsibilities, and shape the future of Campana \& Schott Inc. In addition, take advantage of our generous personal training budget.
Professional \& Personal
You can expect excellent project work and a trusting atmosphere with your colleagues supervisors where you are challenged to contribute to impactful projects across the Project, Program, and Portfolio Management service spectrum of our NY\-based team.
Live Collaboration
Mentoring from senior professionals and engagement in an active exchange of knowledge with over 600 consultants worldwide in forums and special expertise groups.
Compensation
The salary range for this position is $ 84,000 to $ 96,000 per year and total compensation for this role includes base salary, annual discretionary performance bonus, and a comprehensive benefits package described below.
We expect total annualized compensation for New York City\-based employees to be approximately the following:
- Base salary between $ 84,000 \- $ 96,000 p.a.. Placement within this range will vary based on experience and skill level
- Annual discretionary performance bonus between 0\-10%
In addition, we offer a comprehensive benefits package, including:
- 25 days of PTO p.a.
- 10 company paid holidays p.a.
- 5 days of professional development p.a.
- Option for employees to make personal contributions to a 401(k) plan, as well as 401(k) matching offered by Campana \& Schott
- Attractive health benefits
- Annual company trip and regular team events
All qualified applicants will receive consideration for employment without regard to age, race, creed, color, national origin, ancestry, marital status, sex, affectional or sexual orientation.
Preferred Qualifications
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- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field
- 1–3 years of hands\-on programming experience
- Strong Python skills with experience building backend services or automations (FastAPI, Flask, or similar frameworks)
- Demonstrated experience working with LLMs: prompt engineering, tool calling, or building AI\-integrated applications
- Experience with cloud platforms and cloud\-hosted services
- Proficiency with APIs and relational databases
- Strong communication skills \- you communicate your ideas effectively in a client meeting
- A builder's mindset \- you'd rather ship something imperfect and iterate than wait for the perfect plan
Nice\-to\-Haves
- Experience with React/TypeScript or other frontend frameworks
- Familiarity with Git workflows, CI/CD pipelines, and software development best practices
- Understanding of ETL pipelines or scalable data workflows
- Previous experience in a client\-facing or collaborative team environment
- Interest in agentic AI patterns, conversational interfaces, or emerging AI tooling
Do you have questions regarding your application?
Please contact us!
career@campana\-schott.com
Please visit us on LinkedIn
Salary Context
This $84K-$96K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Campana & Schott Business Services GmbH, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($90K) sits 50% below the category median. Disclosed range: $84K to $96K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Campana & Schott Business Services GmbH AI Hiring
Campana & Schott Business Services GmbH has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $96K - $180K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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