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About This Role
#### About ChapsVision \& Sinequa
We build the enterprise platform for AI\-powered search and agentic orchestration. The goal is straightforward: give every employee fast, governed access to the right information, in the right context, when they need it, with tight control over how AI uses it.
As part of the North America Professional Services team, you'll be the Forward Deployed Engineer (FDE) embedded on\-site with one of our largest and most strategic customers, a global manufacturing leader running a multi\-year co\-innovation program to put agentic AI in the hands of its workforce at scale. It's a hybrid role: business lead and technical lead in one. You'll own the translation of the customer's priorities into Sinequa delivery, and of platform capability into outcomes its teams actually adopt. You'll build production\-grade applications, help shape the platform roadmap from the field, and work across the customer's stakeholders, its embedded delivery team, and our R\&D group.
What Makes This Role Different
A lot of forward\-deployed roles give you a set playbook and a rotating list of accounts. This one is set up differently.
You'll own a single flagship account from start to finish, building against real production data (drawings, specifications, parts, PLM) rather than demo environments. And because this is the deployment that shapes how we scale, what you work out here will become the blueprint for the next wave of strategic customers. The strongest parts of what you build can make their way into the core platform roadmap, so your field work reaches every customer we have.
You'll have real ownership of the account, with the platform, the R\&D relationships, and company resources behind you.
What You Will Do
- Embed with the customer: Sit on\-site at the customer's headquarters, working day\-to\-day with its business stakeholders, its own program team, and technical leadership. You'll be the main point where account strategy and technical delivery come together.
- Translate in both directions: Turn business priorities into a delivery plan you can actually build, and turn platform capability into outcomes that work for the people using them. A lot of the value here is judgment and translation, not just code.
- Lead technical discovery: Run the architecture discussions, whiteboarding, and discovery needed to map a complex data ecosystem across PLM, drawings, specifications, and parts data spanning several business units.
- Resolve blockers at the desk: Work through a rigid enterprise environment with high interpersonal instincts. You're comfortable walking over to a stakeholder's desk to unpack an integration roadblock and get people to agreement.
Build Governed Agentic Workflows \& Search Solutions
- Ship production artifacts: Write clean, production\-grade code for complex components like Model Context Protocol (MCP) servers, stateful sub\-agents, and deterministic agent skills on our agent orchestration platform.
- Model\-agnostic design: Build workflows that use the right frontier LLM for the job (Claude, Gemini, GPT, or specialized open\-source models), tuned for cost, context limits, and latency.
- Optimize search at scale: Manage high\-scale indexing and search pipelines for a user population in the tens of thousands. That means search\-engine performance work, document chunking, and real\-time relevancy tuning across a range of data silos, including the PLM\-sourced technical content (for example, PTC Windchill) that sits at the core of this program.
- Build in strong guardrails: Design clear process guardrails and required Human\-in\-the\-Loop (HITL) checkpoints, including SME validation, role\-based access, and a capability maturity lifecycle, so autonomous agents working in low\-margin\-for\-error settings stay safe, predictable, and auditable.
Bridge Delivery and R\&D, and Scale the Product
- Bridge PS and R\&D: Sit between Professional Services delivery and core R\&D. Carry escalation authority and roadmap visibility, and route what you find in the field into structured product feedback.
- Accelerate the handover: Bring the product depth that helps the customer's embedded team take over more of the work over time. Part of doing this job well is gradually handing it off so their team can run it without you.
- Champion safety \& reliability: Keep a high bar for data privacy, making sure native enterprise document\-level permissions hold up end\-to\-end through the retrieval and AI execution loop.
You May Be a Good Fit If You Have
- Agentic platform depth: Hands\-on experience with agentic platforms and agent orchestration matters most for this role. If you've done this kind of work before, you'll get up to speed faster. If you haven't, we'll want to see that you've ramped quickly on complex proprietary platforms in the past.
- A proven track record (8\+ years): Time in a highly technical, customer\-facing role such as Forward Deployed Engineer, Solutions Engineer, or client\-facing Software Engineer. We care more about what you've shipped and the trust you've earned than the exact number of years. Former technical founders are also highly encouraged to apply.
- Account\-leadership range: You can run a strategic account relationship, not just close out tickets. You can talk governance risk with a Director in the morning and debug a retrieval pipeline with a developer in the afternoon, and hold your own in both rooms.
- Production programming: Solid, production\-grade proficiency in Python and C\#, with working TypeScript for front\-end integration, and clean, maintainable code. We value judgment, translation, and account leadership as much as raw coding speed.
- Production AI experience: You've shipped AI\-powered systems in the real world, things like prompt engineering, agent development, evaluation frameworks, and deployment at scale, not just prototypes.
- Search \& retrieval expertise: Deep, practical experience with enterprise search engines, relevancy tuning, and vector databases (for example, Elasticsearch, OpenSearch, Pinecone). You can diagnose and tune precision and recall and re\-ranking pipelines when a query misses.
- Production agentic orchestration: Hands\-on experience building stateful multi\-agent workflows, managing context windows, and wiring up structured tool\-calling and API integrations with modern frameworks (for example, LangGraph, AutoGen, CrewAI, LlamaIndex).
- Manufacturing domain depth \& PLM: Experience in manufacturing settings, indexing very large structured and unstructured enterprise document lifecycles. Direct experience with PTC Windchill is a real advantage.
- High\-context communication: You can move comfortably between deep technical debugging with developers and higher\-level, risk\-focused conversations with corporate directors and compliance officers.
How We Work With AI
We're an AI company, and AI fluency is part of how we work day\-to\-day, not a side skill. You'll use frontier models regularly to move faster and do better work, and we'd like you to have a point of view on where AI helps and where it gets in the way. The interview includes a short, practical AI exercise so we can see how you think about designing and using it. Bring whatever tools and workflows you already use; there's no single right way to do it.
Why Join Us
- Real, measurable impact: You'll work on a program where our agentic platform already does better than the in\-house tools it replaced, on data that general\-purpose AI can't reach.
- Real ownership: You'll own the technical relationship with a global manufacturing leader and have the room to decide how the work gets done, with the platform behind you.
- A direct line to the platform: You'll work closely with our core Product and R\&D teams, and what you learn in the field feeds into what we build next.
- Hard, interesting work: Productionized agentic architectures, model\-agnostic systems, and governed enterprise AI, at global scale.
*We offer a full benefits package: medical, dental, and vision coverage, a 401(k) plan, a generous time\-off policy, and annual learning and wellness stipend.*
Working Conditions
Location: On\-site at the customer's headquarters in the U.S. Midwest (Columbus, Indiana area). Typically, four days on\-site and one day remote. Local residence or relocation is required.
*We are currently unable to consider candidates who require, or will require in the future, sponsorship for work authorization. Applicants must be authorized to work in the US on a permanent and ongoing basis without the need for current or future employer\-sponsored work authorization.*
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 CHAPSVISION, 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.
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.
CHAPSVISION AI Hiring
CHAPSVISION has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbus, IN, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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