Role Description We are looking for an experienced MLOps / Cloud Engineer with a strong background in building and operating cloud-based AI/ML platforms in production environments. The role focuses on designing scalable infrastructure, enabling end-to-end ML workflows, and supporting modern GenAI/LLM solutions. Start Date: ASAP Location: Remote (EU-based) Language: English Contract Type: B2B Responsibilities - Design, build, and operate cloud-based AI/ML platforms in production environments - Develop and maintain scalable MLOps pipelines for end-to-end ML workflows - Implement and optimize CI/CD pipelines for ML and software delivery (e.g., GitHub Actions) - Manage and provision infrastructure using Infrastructure as Code (Terraform) - Deploy, manage, and optimize containerized applications using Docker and Kubernetes (EKS) - Work with AWS and Azure services, including ML services (e.g., SageMaker, Bedrock) - Implement monitoring, logging, and alerting solutions (Prometheus, Grafana, Loki, ELK) - Ensure security best practices across cloud infrastructure and CI/CD pipelines - Support model lifecycle management including model registry, performance monitoring, and data quality tracking - Collaborate with cross-functional teams to deliver robust and scalable AI/ML solutions - Analyze existing codebases and suggest improvements and refactoring where needed Requirements - Hands-on experience with AWS and/or Azure cloud platforms - Proven experience with Kubernetes and Docker in production environments - Strong knowledge of Terraform (Infrastructure as Code) - Experience with CI/CD pipelines (e.g., GitHub Actions) - Proficiency in Python and solid understanding of software engineering principles and architecture - Experience with LLM / GenAI solutions and ML platforms (e.g., SageMaker, Bedrock) - Strong understanding of ML concepts and algorithms, with practical implementation experience - Experience with MLOps tooling and architecture (e.g., Kubeflow, model registry, monitoring) - Knowledge of monitoring and logging tools (Prometheus, Grafana, Loki, ELK) - Understanding of security best practices in cloud and DevOps environments Nice to Have - Experience with enterprise-scale projects and environments - Familiarity with advanced Kubernetes features (e.g., operators) - Experience with performance optimization of Docker images - Exposure to tools like Dynatrace

Job Requisition ID # 26WD96514 Position overview We build AI-driven software that helps water and AEC customers make operational decisions, plan and design — via modelling, optimizing, forecasting, simulation, and automation. You’ll work on production ML systems and backend services that integrate with product data via modern APIs and Model Context Protocol MCP tooling. The role blends machine learning data science, LLM agentic workflows and code generation, and software engineering to deliver reliable, scalable functionality to our customers. Candidates are expected to keep current with modern applied AI techniques. Responsibilities - Implement, integrate, and optimize ML systems for forecasting, anomaly detection, simulation surrogates, and decision support. - Build production inference pipelines, APIs, and services - Design LLM applications (RAG, tool-calling, contextual Q&A) and agentic workflows that orchestrate multi-step tasks — including agentic code generation to automate model-driven actions. - Implement MCP routes, schemas, and integrations to connect models with structured product data and tools. - Ensure reliability with CI/CD, unit/integration/performance testing, observability, and monitoring (model drift, metrics, logs). - Collaborate with platform/cloud teams to deploy models and troubleshoot integrations. - Stay current with state-of-the-art AI applications and contribute improvements to models, tooling, and AI engineering practices. Minimum Qualifications - Bachelor’s or Master’s in CS/Engineering/Data Science or equivalent experience. - 5+ years combining software development and AI/ML for production systems. - Experience with ML libraries such as scikit-learn, PyTorch, Ray. - Experience building with APIs, services, containers, CI/CD. - Familiarity with time-series/sensor/simulation data and basic cloud platform services - Good teamwork, independent learning Preferred Qualifications - Data modeling, optimization, RL, numerical simulation - Practical LLM-application experience, vector search, RAG, MCP - Development of multi-tenant SaaS ML systems - AEC or civil industry experience or background Learn More About Autodesk Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made. We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world. When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us! Salary transparency Salary is one part of Autodesk’s competitive compensation package. Offers are based on the candidate’s experience and geographic location. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package. Diversity & Belonging We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/diversity-and-belonging Are you an existing contractor or consultant with Autodesk? Please search for open jobs and apply internally (not on this external site).

• Partner with product managers and business stakeholders to define technical goals and outcomes for the ML team • Coordinate cross-functional collaboration and delivery for ML subrogation initiatives • Lead the team to deliver cutting-edge ML features using best-in-class software engineering practices • Establish industry-leading standards for ML development and deployment

DescriptionSprout Social empowers businesses worldwide to harness the immense power and opportunity of social media in today’s digital-first world. Processing over one billion social messages daily, our platform serves up essential insights and actionable information to over 30,000 brands, informing strategic decisions that drive business growth and innovation, and fostering deeper, authentic connections to their end customers. Our full suite of social media management solutions includes comprehensive publishing and engagement functionality, customer care solutions, influencer marketing, connected workflows, and business intelligence. We're actively weaving AI throughout our products to drive our business’s growth trajectory. What you’ll do - Build and maintain infrastructure using AWS, Terraform, and Kubernetes to support AI/ML at scale, including Generative AI applications. - Manage the end-to-end lifecycle of machine learning models, ensuring observability and tooling support both scale and speed. - Execute at scale while staying nimble enough to keep up with new capabilities being offered by social network APIs. - Improve processes and champion ideas that matter while holding the team accountable to high code quality and engineering standards. - Support our AI/ML Scientists by developing tooling to streamline model development and deployment. What you’ll bringWe’re looking for a creative, collaborative, pragmatic, highly motivated, and impact oriented technical leader to join our team in building great software. If you can solve hard problems, deliver quality server-side software, and confidently guide your peers to learn from and teach each other, we’d love to talk with you! The minimum qualifications for this role include: - 5+ years of experience developing and supporting AI/ML software in a production environment. - 5+ years of experience programming in object-oriented languages such as Java, Python, or C++. - Impact-oriented mindset with an interest in stability at scale and a willingness to engage in feature development. Preferred qualifications for this role include experience: - 3+ years of experience developing and supporting scalable, distributed backend services. - 3+ years of experience building and supporting GPU-heavy services. - 1+ years of experience with LLMs / Generative AI, including managing their unique costs, constraints, and observability challenges. - 1+ years of experience with Infrastructure-as-Code (Terraform) and container orchestration (Kubernetes) within AWS environments. How you’ll growWithin 1 month, you’ll plant your roots, including: - Complete Sprout’s New Hire training program alongside other new Sprout team members. - Get acclimated to the team's current Mission, Goals, and Objectives along with future product roadmaps. - Become familiar with the team’s existing deployment patterns and the ML Ops tooling ecosystem. Within 3 months, you’ll start hitting your stride by: - Decomposing work into small, similarly sized units and working with your squad to prioritize quarterly team goals. - Setting up initial software for model deployment and monitoring of ML models. Partnering with the Infrastructure team to deploy an existing ML model in Kubernetes. Acting as the domain owner for new projects and writing necessary design documents. Within 6 months, you’ll be making a clear impact through: - Rolling out monitoring and alerting tools to identify problems before they affect users. - Helping deploy new ML models processing hundreds of millions of messages a day. - Identifying technical debt and performance bottlenecks, and executing plans to improve the code. - Collaborating effectively across the organization to ensure big-picture alignment. Within 12 months, you’ll make this role your own by: - Becoming the go-to expert of ML Ops at Sprout. - Developing repeatable deployment patterns for data scientists to train, deploy, and evaluate batch, REST, and event-based ML services. - Owning cross-organizational projects and mentoring junior engineers to help them level up technically. - Surprising us! Use your unique ideas and abilities to change your team in beneficial ways. Our Benefits Program We’re proud to regularly be recognized for our team, product, and culture. We invest in our team with a comprehensive, competitive benefits program: - 100% Employer-Paid Health Benefits: We cover 100% of the premiums for you and your eligible dependents, including comprehensive medical, dental (basic & major), vision, life insurance, and disability. - Generous Paid Time Off: 25 days of vacation annually, plus 5 paid sick days, all public holidays, and additional company-wide Rest & Recharge days. - Premium Mental Health Support: Full, free access to Modern Health for you and your dependents, including coaching, therapy sessions, and digital wellness resources. - Annual Lifestyle Stipend: A $950 CAD annual Lifestyle Spending Account to spend on your physical, mental, and financial well-being. - Remote Work Support: A one-time $550 USD (equivalent) stipend to set up your home office, plus a monthly $50 USD (equivalent) stipend for internet. - Personalized Financial Wellness: No-cost, confidential access to financial experts through Your Money Line to support your personal financial goals. - Family & Care Support: Access to subsidized child and eldercare options through Care.com. - Charitable Giving: A company match for your donations to eligible organizations. *This list is for informational purposes only. Benefit offerings are discretionary and subject to change and do not constitute a contract or guarantee of benefits. Individual base pay is based on various factors, including work location, relevant experience and skills, the responsibility of the role, and job duties/requirements. The base pay range for this role is $132,000 (min) - $181,500 (max) CAD annually. The listed ranges represent earning potential in this position. These ranges were determined by a market-based compensation approach; we used data from trusted third-party compensation sources to set equitable, consistent, and competitive ranges. We also evaluate compensation bi-annually, identify any changes in the market and make adjustments to our ranges and existing employee compensation as needed. Base pay is only one element of an employee's total compensation at Sprout. Every Sprout team member has an opportunity to receive restricted stock units (RSUs) under Sprout’s equity plan. We have outlined the various components to an employee’s full compensation package here to help you to understand our total rewards package. Candidates for this remote work opportunity must be based in either Alberta, British Columbia or Ontario. If you are based in another location within Canada, we aren’t able to hire in your location at this time. #LI-Remote Sprout Social Inc. and its subsidiaries process personal data submitted through your application to assess your qualifications for employment and to inform our hiring decision and, where applicable, for required governmental reporting. For more information, please review Sprout's Global Applicant Privacy Notice.