Role Description The Applied AI Engineer is a hands-on builder who sits at the intersection of AI engineering, operational analytics, and business process expertise. This role designs, prototypes, and operationalizes AI and analytics solutions that automate work, sharpen decision-making, and measurably improve performance across the organization. - Embed directly with operational leaders to translate real-world business challenges into working proofs of concept. - Independently build end-to-end, including the data, modeling, and lightweight infrastructure needed to demonstrate value quickly. - Partner with the data engineering team to harden, scale, and operationalize solutions for production use. - Requires fluency in modern AI tooling, rigorous analytical thinking, and operational context. - Combines technical depth with business fluency to work effectively across stakeholders, data, and code. Qualifications - Bachelor’s degree in Analytics, Data Science, Computer Science, Engineering, or a related field. - 4–7 years of experience in analytics, data science, or AI/ML engineering, with at least 2 years building and deploying ML or AI solutions. - Strong proficiency in Python and SQL, including writing maintainable, tested code beyond exploratory notebooks. - Hands-on experience building applied AI or ML solutions — e.g., predictive models, NLP, or LLM-based applications. - Demonstrated ability to build end-to-end proofs of concept independently. - Experience partnering with data engineering or platform teams to take prototypes into production. - Experience working with large datasets in modern analytics platforms such as Databricks. - Demonstrated ability to translate operational problems into analytical and AI approaches that deliver measurable business outcomes. - Strong communication skills with non-technical stakeholders. Requirements - Design, build, and iterate on applied AI and machine learning solutions. - Independently build end-to-end POCs to validate AI and analytics ideas quickly. - Partner with the data engineering team to harden, scale, and operationalize solutions. - Define success metrics, design evaluations, and quantify business impact. - Analyze operational data, workflows, and performance trends. - Work directly with leadership and frontline operators to understand workflows. - Prepare, clean, and structure datasets for analytics and AI workflows. - Develop, test, and deploy analytics and AI solutions within the Databricks Lakehouse environment. - Partner with field and operational teams to pilot and drive adoption of AI tools. - Apply practical judgment around model limitations, bias, and human-in-the-loop design. Benefits - Health, vision, and dental insurance. - Flexible spending accounts and health savings accounts. - Retirement savings plans. - Life and disability insurance programs. - Paid and unpaid time away from work. - Competitive pay based on various eligibility factors.

Role Description As a Principal AI/ML engineer, this technical leader will be responsible for the design and implementation of the cloud-native AI/ML engineering platform (supporting both GenAI and traditional AI/ML technology and applications). This technologist will drive the technology development and architecture in these areas to ensure the platforms, systems, tools, models, and services generated from this team are technically sound with strong security, quality, reliability, usability, scalability, performance, efficiency, and operability to serve the fast growing needs of Wex and customers. This Principal AI/ML Engineer will have technical ownership of WEX’s AI Engineering implementation, including but not limited to architecture, technical direction, engineering best practice, and quality/compliance. This also includes proper and effective architectural integration with other platforms, products, and services across the company. The vision behind Wex’s AI/ML engineering is to create a unified, scalable, and intelligent data and compute infrastructure that empowers our organization to unlock the full potential of our data. This role can also be in Chicago, Portland, Maine (Wex HQ), or Boston. The ideal candidate will be a hands-on leader with deep technical expertise in building and scaling data lake environments, coupled with strong people leadership and communication skills to drive alignment across the organization. Responsibilities - Lead and drive the development of technology and platform for the company's AI/ML engineering needs, ensuring functional richness, reliability, performance, and flexibility. - Help design the architecture and lead the implementation of the AI/ML infrastructure, platform, and services. - Challenge the status quo and hold a high bar to drive for better technical solutions in the broad risk area of Wex. - Guide your team to leverage data and/or AI technology or tools for high productivity and better solution quality. - Partner with other senior engineers across the organizations to understand business needs and develop technical solutions. - Collaborate with information security teams to ensure data security and privacy. - Partner with solutions architecture and LOB product and engineering orgs on integrations of the AI/ML Platform. - Engage with external partners and vendors to evaluate technologies and services that complement the company’s AI/ML strategy. - Create, prioritize, communicate, manage, and execute roadmaps, project plans, and commitments. - Report on the status of development, quality, operations, and system performance to management and partners. - Drive the technical vision and strategy of AI/ML infrastructure to serve business needs. - Provide technical guidance and effective training & mentorship to the engineering teams. - Set clear, measurable quality goals for your group in a data-driven way. - Partner with the security org and SRE org to ensure continuous improvement and meeting of security requirements. - Take strong financial responsibility for your team. - Hold yourself and your team accountable for delivering quality results using defined OKRs. - Advise Senior Leadership team on highly complex situations impacting the organization. - Provide strategic thought leadership on business initiatives using your deep technical and business industry expertise. Qualifications - 15-20 years of software design and development experience at a large scale. - Strong software development skills in chosen programming language; willing to learn Java and Python. - Experience in building machine learning supervised and unsupervised models; Reinforcement Learning is a plus. - Experience in using LLM models is a plus. - Broad understanding of distributed computing frameworks like Ray and Spark. - Demonstrable experience in Kubernetes and Terraform. - Experience with Sagemaker is a plus. - Broad understanding of cloud architecture tools and services, such as S3, EMR, Kubernetes, Lambda functions; experience in AWS and Azure is highly desirable. - Deep technical expertise in architecture design, development, deployment, and production operation. - Rich experience and deep expertise in Big Data and large scale data platforms, especially in AI and ML. - Highly experienced in CI/CD automation. - Excellent analytical skills; capability in identifying and resolving complex problems. - Understanding of and experience in payment organizations is a plus. - Strong written and verbal communication skills. - Bachelor degree in Computer Science or a related field; a higher degree is a plus. Leadership & Personal Characteristics - Demonstrates a collaborative, engaging, mission-driven style with good listening skills. - Champions a culture of high standards, embraces change, and challenges the status quo. - Able to operate in a fast-paced entrepreneurial environment with a hands-on approach. - Communicates effectively in small and large group formats. - Possesses a high level of intellectual curiosity and learning agility. - Understands nuance and how to drive change through influence across a complex business. Benefits - Health, dental, and vision insurances. - Retirement savings plan. - Paid time off. - Health savings account. - Flexible spending accounts. - Life insurance. - Disability insurance. - Tuition reimbursement. Pay Range $250,300.00 - $289,000.00

Role Description Serve as the engineering lead for Agentic AI delivery across Supply Planning and Manufacturing — owning the design, development, and deployment of production-grade AI agent solutions. - Architect and build multi-agent AI systems using Azure AI Agent Service, AutoGen, Semantic Kernel, and/or LangChain/LangGraph — including orchestrator-executor patterns, tool calling, memory management, and agent coordination. - Implement the MCP to surface enterprise data as structured context for AI agents operating in supply chain and manufacturing workflows. - Build and deploy generative AI solutions on Azure AI Foundry — RAG-based knowledge agents, decision support for forecasting and capacity planning, and document intelligence for maintenance work orders and recipes. - Design and deliver AI copilots and topic-based agents using Microsoft Copilot Studio — enabling Supply Planning and Manufacturing teams to access insights and take action directly from Teams and Outlook. - Act as the AI delivery owner for agentic use cases — scoping business problems with stakeholders, defining agent capabilities and tool surfaces, prioritizing the roadmap, and driving adoption. - Apply emerging agentic AI patterns — including ReAct, Plan-and-Execute, reflection, and human-in-the-loop — for supply chain and operational use cases. - Partner with Supply Chain leadership, Demand Planning, Process Engineering, Maintenance Ops, and Plant teams to identify, scope, and deliver AI use cases that influence operational decisions. - Define and maintain AI agent governance — prompt versioning, tool auditing, evaluation frameworks, observability, and safety guardrails for production deployments. - Develop on Azure Databricks — PySpark and SQL against gold/platinum Delta tables, notebooks for transformation and feature work, and orchestration via Workflows. - Build and maintain Power BI reports and semantic models that serve as grounding data for AI agents and executive dashboards across Supply Planning and Manufacturing. - Own Supply Chain AI metrics alignment cadence — keeping priorities, status, and roadblocks visible to Supply Chain and Manufacturing leadership. - Mentor analysts and engineers on agentic AI design patterns, MCP, and AI delivery best practices. Qualifications - Master’s degree in Mathematics, Computer Science, Data Science, Information Systems, Engineering, or a related field with 5+ years of relevant analytics / AI experience, OR - Bachelor’s degree in Chemical, Industrial, Computer Science, or related fields with 8+ years of relevant analytics / AI experience. Requirements - Hands-on experience with the MCP — building or consuming MCP servers/clients; ability to expose enterprise data sources (databases, APIs, SharePoint, ERP) as MCP tools for AI agents. - Hands-on experience with multi-agent system design — designing and implementing multi-agent architectures; orchestrator-executor patterns, tool calling, memory management, and agent coordination using AutoGen, Semantic Kernel, LangChain/LangGraph, or Azure AI Agent Service. - Strong Python engineering skills — building production-grade AI agents and pipelines, including REST API integration, prompt versioning, evaluation frameworks, and observability for LLM-based systems. - Compulsory — must have hands-on experience with two or more of the following: - Azure AI Foundry (RAG pipelines, prompt flows, agent service) - Microsoft Copilot Studio (agents, topics, actions, Power Automate integration) - Microsoft 365 Copilot extensibility (plugins, connectors, Graph APIs) - Microsoft Power BI (DAX, semantic modeling, performance tuning) - Strong proficiency in Databricks (Python, SQL, Delta Lake, PySpark, notebooks). - Strong functional understanding of Supply Planning (S&OP, demand/supply planning, inventory, order management) and/or Manufacturing (plant maintenance, capacity planning, OEE). - Experience with SAP ECC / S/4HANA supply chain and manufacturing modules (MM, PP, SD, PM). - Ability to translate business problems into agentic AI solutions and communicate clearly to technical and executive audiences. - Strong collaboration and stakeholder management skills in cross-functional environments. Preferred Qualifications - Experience deploying AI agents in production — evaluation frameworks, safety guardrails, logging, and human-in-the-loop workflows. - Familiarity with agentic design patterns (ReAct, Plan-and-Execute, reflection, structured tool outputs). - Familiarity with knowledge graphs or graph databases (e.g., Neo4j) for agent reasoning and grounding. - Strong Power BI experience — semantic modeling, performance optimization, executive dashboard design. - Experience in chemicals, manufacturing, or process industries. - Experience with Palantir Foundry (pipelines, ontology, Workshop, AIP). - Exposure to MLOps on Azure (Azure ML, MLflow, Databricks Asset Bundles, CI/CD for analytics). - Experience designing operational KPI frameworks (MAPE, OTIF, service level, OEE, downtime). - Experience with containerization (Docker), version control (Git), and modern software engineering practices. Other We are an Equal Opportunity, Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to gender, pregnancy, race, national origin, religion, age, sexual orientation, gender identity, veteran or military status, status as a qualified individual with a disability or any other characteristic protected by law. To be considered for this position candidates are required to submit an application for employment through our career site and, be at least 18 years of age. Any offer of employment will be conditioned upon successful completion of a drug test and background investigation, as well as authorization for the Company to conduct additional periodic background checks as required by the Chemical Facility Anti-Terrorism Standards (CFATS) or regulations adopted by the department of Homeland Security or other regulatory agencies. A prior criminal record is not an automatic bar to employment, and the Company will conduct an individualized assessment and reassessment, consistent with applicable law, prior to making any final employment decision.

Role Description A well-funded seed-stage startup building the next generation of autonomous trading technology. You are building the intelligence layer on top of a purpose-built execution system for AI agents operating with real capital around the clock. What You'll Own - Learning System & RL Loop (~70%): - Design and implement the pipeline that connects live trade outcomes back to strategy improvement — signal quality, position sizing, timing, risk parameters. - Build the evaluation framework that separates genuine predictive signal from noise across agents, market conditions, and configurations. - Automate the strategy generation and testing cycle — the system should explore new configurations, validate them against real fleet data, and surface deployment candidates. - Detect regime shifts in market conditions and adapt fleet behavior accordingly. - Decompose every trade into its component drivers — signal quality, execution efficiency, exit timing — and wire those attributions back into strategy design. - Manage fleet-level coordination: concentration risk, capital allocation, and the exploration vs. exploitation balance. - Build the telemetry and data capture layer that makes all of the above possible. - Model & Inference Infrastructure (~30%): - Own the build-vs-buy decision on model hosting — evaluate proxied external APIs versus fine-tuned models on owned infrastructure and execute the chosen path. - Determine whether domain-specific training on trading data meaningfully outperforms prompted general-purpose models — then build the pipeline to act on that answer. - Optimize inference for the specific demands of a large autonomous agent fleet: concurrent agents, structured outputs, cost efficiency at scale. - Build the agent telemetry layer capturing every decision, signal score, and evaluation across the fleet. Qualifications - A production closed-loop system — model outputs drove real-world actions, outcomes were measured, and that feedback automatically improved the next decision. - Practical RL or online learning experience — you understand the challenges of learning from real-world feedback rather than static datasets. - Full-stack ML ownership — you build the pipeline, deploy the model, and own the outcome; Python primary, comfortable with Go or TypeScript in production services. - High-stakes sequential decision-making domain experience — finance preferred but not required; robotics, autonomous vehicles, game AI, ad bidding, and supply chain all transfer. Nice to Have - LLM fine-tuning and open-source model serving in production (vLLM, TGI, PEFT/LoRA). - Multi-agent system design. - Financial ML — signal generation, execution optimization, portfolio construction. - Onchain or DeFi experience. Interview Process - Fast — target first call to offer within two weeks. - Intro call with founders (60 min) — fit, motivation, your closed-loop experience. - Technical deep-dive (60 min) — open-ended system design, no right answer, evaluating how you think. - Paid trial project (1 week, part-time) if needed — real problem, compensated.