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About This Role
Wego Chemical Group is a global distributor and sourcing partner of chemicals and ingredients. We are an established player in the global specialty chemical markets, providing end-use manufacturers across industries in North America, LATAM, & EMEA with end-to-end supply chain solutions from across the globe. Our team has been operating for over 45 years, serving customers in over 25 countries, with 200+ professionals in 9 offices strategically located across 4 continents. Proudly family-owned and operated, Wego is led by two generations of leadership who bring passion, vision, and a long-term commitment to our partners' success.
Job Summary
The AI and Workflow Engineer is responsible for finding, building, and running AI automation across Wego's business processes. Working in a forward-deployed model, this role sits with business teams to identify the workflows and processes AI can automate, then designs and delivers solutions using Claude, Microsoft Power Automate / Power Platform, and Microsoft Copilot. The role connects the AI stack to Wego's core enterprise systems, including Oracle ERP, Oracle OTM, Salesforce, and SAP Concur, and owns each solution from first prototype through production support.
Responsibilities
- Partner with business teams across Wego to map processes, size transaction volumes, and identify and prioritize AI automation opportunities, starting with quick wins.
- Design, build, test, and deploy AI agents, workflows, and applications using Claude, Power Automate / Power Platform, and Copilot, selecting the right tool, or combination of tools, for each task.
- Build and consume REST APIs and webhooks to integrate AI solutions with Oracle ERP, Oracle OTM, Salesforce, SAP Concur, and other business applications.
- Develop Claude skills, connectors (MCP servers), Cowork tasks, and scheduled tasks for repeatable business workflows.
- Build event-driven agents that start automatically from system events, such as a new high-priority service ticket, using API triggers, webhooks, the Claude Agent SDK, and Power Automate.
- Develop applications on the Claude API with structured JSON outputs, using Claude Code as a core development tool.
- Write and maintain Python or JavaScript services, scripts, and user interfaces, using Git for source control and code reviews.
- Work with SQL, vector, and graph databases to prepare, store, and retrieve data for AI solutions.
- Build testing, evaluation, and prompt engineering into every solution to prove accurate output before and after go-live.
- Monitor token usage, cost per run, and model selection to keep solutions cost-effective.
- Apply security and governance controls, including least-privilege access, secrets handling, skill vetting, and human approval checkpoints.
- Work closely with the Enterprise Applications and IT Operations teams on integrations, system changes, and ongoing support.
- Document designs, procedures, and controls, and train business users on new solutions.
- Track emerging AI technologies and best practices and share recommendations with IT and business leaders.
Key Qualifications
- Bachelor’s degree in computer science, software engineering, information technology, data science, artificial intelligence, or a related field preferred; equivalent hands-on experience will be considered in place of a degree.
- 2–4 years of hands-on experience building automation, integration, or AI solutions for business users.
- Track record of shipping AI solutions into production that business users run day to day.
- Proficiency in Python or JavaScript; working knowledge of HTML and CSS.
- Strong SQL skills and experience with vector and graph databases.
- Hands-on experience building and consuming REST APIs and webhooks.
- Experience building with large language models and generative AI, preferably Claude (Claude API, skills, MCP, Claude Code, Agent SDK).
- Experience with Microsoft Power Automate / Power Platform and Microsoft Copilot.
- Understanding of LLM token cost, model selection, and cost optimization.
- Solid software development fundamentals, including testing, debugging, Git version control, and object-oriented programming.
- Knowledge of security practices for AI and integrations, including least privilege, secrets management, and human-in-the-loop approval.
- Experience integrating with ERP or CRM platforms such as Oracle ERP Cloud, Oracle OTM, Salesforce, or SAP Concur preferred.
- Familiarity with cloud platforms (Azure, AWS, or Google Cloud) preferred.
- Strong business sense, with the ability to turn a process problem into a practical automation and explain it to non-technical users.
- Excellent communication skills; self-directed, works well independently and within a team, and is open about skill gaps and how to close them.
Location & Work Arrangement
Great Neck, NY (Wego headquarters). Remote work will be considered for a candidate with an excellent fit who is willing to travel to headquarters on an agreed frequency.
Salary Context
This $120K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Wego Chemical Group, LP, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($122K) sits 42% below the category median. Disclosed range: $120K to $125K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Wego Chemical Group, LP AI Hiring
Wego Chemical Group, LP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Great Neck, NY, US. Compensation range: $125K - $125K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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