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About This Role
##### Integration meets Innovation
At Celigo, we believe integration should empower — not exhaust — innovation. As a modern Integration and Automation Platform (iPaaS), we're on a mission to simplify how companies integrate, automate, and optimize processes. Powered by game\-changing technology like runtime AI and prebuilt, mission\-critical integrations, Celigo is redefining how businesses connect their world.
The Forward Deployed AI Engineer (FDAE) is a pioneering role at the heart of our customers' agentic transformations. Using Celigo's strategic frameworks, you'll conduct workshops and interviews with executive team members to identify critical business processes, core challenges, and AI opportunities that Celigo is uniquely positioned to solve \- then you'll collaborate with Customer build teams to bring those solutions to life.
Embedded directly within Enterprise customer environments, this engineer drives rapid, measurable adoption of Celigo's AI capabilities — including AI Studio, agentic workflows, and MCP server orchestration — by identifying high\-value business processes ripe for automation and personally building production\-grade solutions.
##### What would you do if hired?
- Conduct workshops and strategy sessions with executive teams, present findings, and translate into design documentation and project plans
- Utilize Celigo's AI capabilities — including AI Studio, agentic workflows, and MCP server orchestration
- Coordinate with customer data teams to understand data quality and agentic transformation requirements
- Design intelligent prompts and develop AI agents and solutions that drive real value for customers
- Set up and maintain MCP servers for scalable integration runtime environments
- Design and manage APIs, including defining endpoints, authentication, versioning, and governance best practices
- Co\-build alongside customers and partners, sharing best practices and enablement as you go
- Contribute to proofs\-of\-concept and MVPs that move from sketch to deployable in days, not weeks
- Perform end\-to\-end testing and validation of agentic solutions
- Ensure best practices in performance, security, and governance
- Provide go\-live support, ongoing support, advisory services, and knowledge transfer to customers
- Educate customers on the use of Celigo platform's agentic capabilities
- Mentor junior developers and contribute to solution design reviews
##### Who are we looking for?
Skills \& Abilities
- Strong problem\-solving skills with the ability to troubleshoot complex agentic issues
- Excellent verbal and written communication skills across business, technical, and executive audiences
- Client\-facing professionalism with the ability to lead discussions, training, and advisory sessions
- Self\-starter with strong ownership, attention to detail, and ability to work independently
- Ability to manage multiple priorities in a fast\-paced, client\-driven environment
- Demonstrated business acumen and sound decision\-making skills
- Drive to self\-learn and showcase emerging technologies, including AI and automation, to elevate the practice
Technical Skills
- Strong experience with prompt engineering, using frontier LLM models, API integration and management to solve business problems
- Strong understanding of API lifecycle management, versioning, security, and governance
- Experience integrating ERP systems (NetSuite, Acumatica, Microsoft Dynamics, SAP, etc.)
- Hands\-on experience with cloud/SaaS platforms (Salesforce, Shopify, Amazon, etc.)
- Data transformation expertise (JSON, XML, CSV, EDI, mapping, normalization)
- Understanding of middleware and iPaaS architecture patterns
- Experience integrating and operationalizing AI/LLM services
- Familiarity with agentic architectures and orchestration concepts (e.g., Celigo agentic services, MCP servers)
Education \& Experience
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field is preferred
- 7\+ years of total experience, ideally with 4\+ years of experience in management / technical consulting with direct client engagement
- 3\+ years of experience with AI development (frontier models, agentic services, MCP servers) with at least one production agent you can walk through
- Proven ability to design and deliver scalable, production\-grade integration solutions
- Experience across the full delivery lifecycle (requirements through post\-go\-live support)
- Understanding of technologies such as Node.js, web services (SOAP/REST), and data transport formats (JSON, XML, EDI, flat files)
- Familiarity with enterprise applications (NetSuite, Salesforce, Amazon, Google, Shopify, HubSpot, etc.)
- Strong database fundamentals (SQL, data modeling, querying)
- Knowledge of legacy integration methods (FTP, ACH, fixed\-width files, EDI) is a plus
Celigo reasonably expects to pay a base salary between $145,000 and $160,000 per year for this position. Actual starting base pay will be determined by skills, experience, geographic location, and other non\-discriminatory factors permitted by law. Total compensation may also include variable incentives, benefits, or other perks as outlined in any formal employment offer made.
Celigo is proud to be
- A 2025 Gartner Customers' Choice for iPaaS. The only vendor to receive this award.
- Celigo is a Visionary in the Gartner Magic Quadrant for iPaaS for the second consecutive year
- Celigo is ranked \#1 iPaaS on G2 for multiple quarters and named a Leader in both B2B/EDI and API Management.
- Celigo is a leading intelligent automation platform that puts the power of automation in the hands of every team, unifying workflows from the predictable to the fully agentic in a single platform.
Here you'll experience
- Remote\-first culture, built on trust, collaboration, and transparency
- A high\-growth, inclusive work environment where innovation thrives and ideas are implemented
- Lightspeed learning opportunities to keep you at the leading edge of your field
- Exceptional coworkers who challenge and inspire you daily
- Competitive compensation and benefits, including:
- + Three weeks of vacation (starting year one)
+ Wellness days and holidays to recharge
+ Parental leave and a generous benefits package
+ Monthly tech stipend
+ Recognition and career development opportunities
Diversity, Equity, Inclusion, and Accessibility
As a company, one of the values we hold most dear is fostering a safe, collaborative environment to bring out the best in us, so we created our Taking a Stand Initiative. Our TAS initiative is a volunteer committee open to all Celigans, with representation from underrepresented voices within our company. We believe, unequivocally, that everyone deserves to be in a place where they feel welcome as they are. Learn more about Taking a Stand.
Celigo is proud to be an equal\-opportunity workplace. We are committed to equal employment opportunities regardless of race, color, ancestry, national origin, religion, creed, age, disability, sex, gender, sexual orientation, gender identity, gender expression, medical condition, genetic information, marital status, military and veteran status, or any other characteristic protected by applicable law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
Salary Context
This $145K-$160K 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 Celigo, 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 ($152K) sits 29% below the category median. Disclosed range: $145K to $160K.
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.
Celigo AI Hiring
Celigo has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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