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Company Overview
Arcesium is a global financial technology firm that solves complex data-driven challenges faced by some of the world’s most sophisticated financial institutions. We constantly innovate our platform and capabilities to meet tomorrow’s challenges, anticipate the risks our clients encounter, and design advanced solutions to help our clients achieve transformational business outcomes.
Financial technology is a high-growth industry as change and innovation continue to disrupt the status-quo and prompt major transformation. Arcesium is at a particularly interesting time in our own growth as we look to leverage our successfully established market position and expand operations in pursuit of strategic new business opportunities. We value intellectual curiosity, proactive ownership, and collaboration with colleagues, and we empower you to meaningfully contribute from day one and accelerate your professional development.
Position Summary
Arcesium is building a Corporate AI Transformation team, and you will be one of its few first members. As a Forward Deployed Engineer, you will work closely with business, operations and engineering teams to identify high-impact internal use cases and convert them into working AI solutions that run in production.
This is not a traditional backend or application engineering role. You will operate like a builder, systems engineer, and product thinker rolled into one. You will take ambiguous business problems, break them into solvable workflows, design the agent architecture, and ship production-grade internal tools. You will work with Arcesium's corporate systems, data assets, and AI tech stack to build agents and automations that reduce manual effort, improve decision-making, and scale repeatable work. Success for this role isn’t just measured by shipping a prototype or completing a project, it’s measured by putting solutions into production and driving meaningful adoption and impact.
The ideal candidate brings strong engineering fundamentals, a builder’s mindset, and a passion for using AI to improve how work gets done. This is a high-impact role for someone who wants to build AI solutions that meaningfully improve enterprise processes.
Responsibilities
- Partner with business and functional teams to find the opportunities: workflow automation, AI-assisted decision-making, and work that agents can take on.
- Understand complex operational processes across teams and convert them into structured problem statements, system designs, and technical workflows.
- Build agents, workflow automations, and internal applications on top of Arcesium's corporate data systems, and enterprise tools.
- Rapidly prototype using LLMs, agent frameworks, MCPs. Get something small in front of real users early and iterate on their feedback.
- Design agent architectures that complete business tasks reliably.
- Work with engineering, business, and security teams to take prototypes to move prototypes into scalable, maintainable solutions.
- Evaluate build-vs-buy options for AI workflows and identify the right technical approach for each use case.
- Create reusable components, templates and playbooks that accelerate this work across the firm.
- Define the KPIs, measure the impact, and drive adoption.
Qualifications
- 6+ years of hands-on engineering experience, with a track record of shipping systems that other people depend on.
- Strong engineering fundamentals across APIs, data, and systems design.
- Experience building internal tools or automation systems for business teams.
- The ability to understand a platform quickly, build on top of it, and turn business context into reliable technical solutions.
- Systems thinking. You can hold a whole workflow in your head, see where it is structurally inefficient, and redesign it rather than automate it as it stands.
- A bias to ship iteratively. You get a thin version into real use, learn from it, and improve it, instead of waiting for a complete design.
- Credibility with senior business and technology leaders, and the ability to say hard things clearly to both.
- Comfort with ambiguity and ownership. You will pick your own problems as often as you are handed them.
The expected annual base salary for this position is $200,000 - $250,000. Our compensation package includes variable compensation in the form of a year-end bonus, guaranteed in the first year of hire, benefits including medical and prescription drug coverage, and 401k contribution matching.
The applicable annual base salary paid to a successful applicant will be determined based on multiple factors including without limitation the nature and extent of prior experience and educational background.
Arcesium's Personal Data Privacy Notice for Candidates is linked here.
Recruiting Security
Emails from genuine Arcesium recruiters who are employees of the company will always come from the @arcesium.com domain. In some cases, you may also be contacted by independent search firms engaged to recruit on our behalf; emails from their employees should always come from their firm's applicable domain. We'll never ask for your banking information or any payment as part of the recruiting process. If something seems off or you're contacted by an unexpected third party, please reach out to us at [email protected] (US/UK), [email protected] (India) or [email protected] (Portugal/Sweden).
Arcesium is an equal opportunity employer.
Salary Context
This $200K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Arcesium, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($225K) sits 6% above the category median. Disclosed range: $200K to $250K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Arcesium AI Hiring
Arcesium has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $250K - $250K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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