Software Engineer II - MLOps

Why Solstice Analytics Pro? Help us build solutions that build better lives. At Solstice Analytics Pro, we show up to work every day to make a difference for real people: the home service professionals that support America’s 100 million homes. We’re all about the Pro, and dedicate our days to helping them streamline operations, scale their businesses, and—ultimately—save time so they can be with their families and live well. While our headquarters are based in Denver, Colorado, our team spans the globe, with teammates across Brazil, Poland, the Philippines, Mexico, and beyond. We care deeply about our customers and foster a culture where our company, people and Pros grow and succeed together—no matter where they’re based. Leadership is as focused on growing team members’ careers as they expect their teams to be on creating solutions for Pros. We also offer: Paid holidays and flexible, take-it-as-you-need-it scheduled time off A culture built on innovation that values big ideas, no matter where they come from A MacBook set up and ready from day one, plus a $500 stipend to design your ideal workspace Equity in a rapidly growing startup backed by top-tier VCs TO BE CONSIDERED FOR THIS ROLE, PLEASE SUBMIT AN UPDATED RESUME TRANSLATED TO ENGLISH Role Overview As a Software Engineer II - MLOps, you are a foundational builder who bridges the gap between complex machine learning development and robust core engineering infrastructure. You partner closely with data scientists, product engineers, and infrastructure teams to deploy, monitor, and scale machine learning solutions in production. You are deeply passionate about automation, system reliability, and scaling pipelines that turn raw code into highly available product features. You balance a technical engineering mindset with an operational focus to maximize the reliability of our production systems. Our team is passionate, empathetic, hard working, and above all else focused on improving the lives of our service professionals (our Pros). Our success is their success. What you do each day: Implement robust infrastructure for deploying, monitoring, and managing machine learning models in live production environments Build automated, end-to-end machine learning pipelines focusing on feature engineering, model deployment, and continuous evaluation Collaborate with data scientists and product engineering teams to operationalize complex models and elevate production readiness Develop sustainable continuous integration and continuous deployment pipelines to support reproducible model release workflows Establish comprehensive monitoring, logging, and alerting solutions to ensure peak model performance and system health Support the continuous optimization of system architecture to improve scalability, uptime, and infrastructure cost efficiency Evaluate emerging technologies and operational best practices to integrate meaningful upgrades into the team's engineering stack Document architectural standards, technical processes, and operational procedures to promote cross-team knowledge sharing Qualifications: 3+ years of professional experience in MLOps, Data Engineering, or core infrastructure software roles Demonstrated proficiency in backend programming languages with a strong emphasis on Python Hands-on experience deploying, monitoring, and supporting machine learning models within distributed environments Proven understanding of workflow orchestration systems (i.e. Apache Airflow) Solid understanding of distributed batch or streaming data tools (i.e. Kafka, Spark) Experience implementing automated continuous integration and continuous deployment software delivery workflows Demonstrated ability to leverage AI tools to improve workflows, streamline execution, or enhance outputs Bachelor's degree in Computer Science, Engineering, Mathematics, or equivalent work experience What will help you succeed: Exceptional breadth of interest shown through tangible, self-initiated ventures or deep community involvement; you love trying new things and possess a demonstrated history of successfully pivoting or starting over in life and work Strong communication and collaboration skills when working across cross-functional engineering pods A proactive mindset dedicated to automation and eliminating manual operational engineering bottlenecks High attention to detail when debugging shared infrastructure and pipeline exceptions ✨ Let’s talk numbers! ✨ Our compensation range for this role begins at $4500 USD per month 💵 Founded in 2013, Solstice Analytics Pro helps home service professionals (Pros) streamline every aspect of their business. With easy-to-use tools for scheduling, dispatching, payments, and more, Solstice Analytics Pro enables Pros to save time, grow profitably, and provide best-in-class service. Solstice Analytics Pro’s brand portfolio includes Business Coaching by Solstice Analytics Pro, a business coaching solution for home services businesses. Our brands are united by a singular mission to champion our Pros to success. We support more than 50,000 businesses and have over 2,000 ambitious, mission-driven, genuinely fun-loving teammates across the globe. If you want to do work that impacts real people, supported by a team that will invest in you every step of the way, we’d love to hear from you. Solstice Analytics Pro celebrates diversity and we are committed to creating an inclusive environment. We are an equal opportunity employer and do not discriminate on the basis of gender, race, religion, national origin, ethnicity, disability, gender identity/expression, sexual orientation, veteran or military status, or any other category protected under the law. #LI-Remote Privacy Notice for California Job Candidates - Solstice Analytics Pro Use of Artificial Intelligence in Hiring and Engagement Processes: we may use artificial intelligence (AI)-powered tools to support the review and evaluation of employment and independent contractor applications, including screening and qualification assessments. All final hiring and engagement decisions are made by human representatives, not AI tools.