FBS – Farmer Business Services is part of Farmers operations with the purpose of building a global approach to identifying, recruiting, hiring, and retaining top talent. By combining international reach with US expertise, we build diverse and high-performing teams that are equipped to thrive in today’s competitive marketplace. We believe that the foundation of every successful business lies in having the right people with the right skills. That is where we come in—helping Farmers build a winning team that delivers consistent and sustainable results. Since we don’t have a local legal entity, we’ve partnered with Capgemini, which acts as the Employer of Record. Capgemini is responsible for managing local payroll and benefits. What to expect on your journey with us: - A solid and innovative company with a strong market presence - A dynamic, diverse, and multicultural work environment - Leaders with deep market knowledge and strategic vision - Continuous learning and development This position leads the deployment, implementation, and optimization of machine learning pipelines to solve complex business challenges. The role involves both hands-on work and supervising a team to deliver effective machine learning engineering solutions for a line of business. The position applies in-depth knowledge of policies, procedures, and business objectives to make decisions and guide team. Performs work independently while receiving limited guidance Key Responsibilities - Delivers machine learning ops engineering tasks such as deployment, implementation, optimization, and maintenance of machine learning pipelines and models. - Ensures pipelines support efficient data ingestion, preprocessing, model training, validation, deployment and monitoring. - Implements scalable and robust machine learning solutions that can handle large volumes of data and complex models. - Implements real-time inference with high availability and low latency. - Creates strategic plans within span of control and implements them across one to two business domains. - Ensures seamless integration of pipelines with continuous integration and continuous deployment (CI/CD) tools and workflows. - Supporting and maintaining solutions in production (fixing bugs, make changes as required, maintaining models)   - Collaborates with cross-functional teams to integrate machine learning and business logic-based solutions into production systems - Effectively communicates and applies machine learning engineering value, concepts, and strategies in various scenarios with stakeholders - Recruits, hires, and mentors' top talent to build a high-performing MLOps team. Supervises, coaches, and guides direct reports - Uses advanced knowledge of code management principles to follow architectural and governance guidelines

FBS – Farmer Business Services is part of Farmers operations with the purpose of building a global approach to identifying, recruiting, hiring, and retaining top talent. By combining international reach with US expertise, we build diverse and high-performing teams that are equipped to thrive in today’s competitive marketplace. We believe that the foundation of every successful business lies in having the right people with the right skills. That is where we come in—helping Farmers build a winning team that delivers consistent and sustainable results. Since we don’t have a local legal entity, we’ve partnered with Capgemini, which acts as the Employer of Record. Capgemini is responsible for managing local payroll and benefits. What to expect on your journey with us: - A solid and innovative company with a strong market presence - A dynamic, diverse, and multicultural work environment - Leaders with deep market knowledge and strategic vision - Continuous learning and development This position leads the deployment, implementation, and optimization of machine learning pipelines to solve complex business challenges. The role involves both hands-on work and supervising a team to deliver effective machine learning engineering solutions for a line of business. The position applies in-depth knowledge of policies, procedures, and business objectives to make decisions and guide team. Performs work independently while receiving limited guidance Key Responsibilities - Delivers machine learning ops engineering tasks such as deployment, implementation, optimization, and maintenance of machine learning pipelines and models. - Ensures pipelines support efficient data ingestion, preprocessing, model training, validation, deployment and monitoring. - Implements scalable and robust machine learning solutions that can handle large volumes of data and complex models. - Implements real-time inference with high availability and low latency. - Creates strategic plans within span of control and implements them across one to two business domains. - Ensures seamless integration of pipelines with continuous integration and continuous deployment (CI/CD) tools and workflows. - Supporting and maintaining solutions in production (fixing bugs, make changes as required, maintaining models)   - Collaborates with cross-functional teams to integrate machine learning and business logic-based solutions into production systems - Effectively communicates and applies machine learning engineering value, concepts, and strategies in various scenarios with stakeholders - Recruits, hires, and mentors' top talent to build a high-performing MLOps team. Supervises, coaches, and guides direct reports - Uses advanced knowledge of code management principles to follow architectural and governance guidelines

• Serve as the technical lead for our SurvalentONE SCADA platform. • Administer all critical aspects of the SCADA environment, including data platform development, alarm management, and application design and configuration. • Ensure high availability and platform resilience, via mature architecture choices, strong monitoring, and SLAs. • Monitor system performance and proactively address stability, latency, alarm flooding, and data integrity issues. • Lead platform patching, upgrades, licensing management, and version control. • Ensure lean but structured change management to provide fast feature delivery with minimal operational disruption. • Lead root cause investigations for system-level failures or platform degradation. • Collaborate closely with key stakeholders to optimize data pipelines and infrastructure. • Develop SCADA lifecycle processes and maintain ISA-112 alignment. • Develop and maintain enterprise SCADA standards spanning system philosophy, alarm management, naming conventions, network architecture, and automation. • Produce annual SCADA performance and roadmap reports. • Design and implement scalability improvements as fleet expands. • Document and maintain complete SCADA topology and system architecture. • Troubleshoot complex SCADA-related incidents impacting the Control Center, including communications failures (DNP3, Modbus, IEC 61850), data corruption, nuisance alarms, application anomalies, and disaster recovery. • Support integration and mapping of RTACs and other automation controllers into Survalent. • Analyze power system event data and assist in disturbance analysis when required. • Provide SCADA reference design standards and templates for Construction Engineering. • Define required points lists and data mapping standards for new projects. • Support factory testing and integration validation prior to project COD. • Ensure seamless integration of new facilities onto the Survalent platform in collaboration with Software and Control Center teams. • Define and verify proper data ingestion, alarm alignment, and HMI integration prior to operational handoff.

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may:  - Design original computational physics problems that simulate real physics research workflows; - Create problems requiring Python programming to solve (using Numpy, SciPy, Sympy); - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks); - Develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics; - Base problems on real research challenges or practical applications from physics practice; - Verify solutions using Python with standard physics simulation libraries; - Document problem statements clearly and provide verified correct answers. What we look for This opportunity is a good fit for optical engineers with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:   - Degree in Physics (Theoretical, Experimental, or Computational) or related fields; - Python proficiency for numerical validation. MATLAB, R, C, SQL, Numpy, Pandas, SciPy, domain-specific libraries, Stata or knowledge of any programming language can be equivalent; - 2+ years of professional experience: applied, research, or teaching experience is applicable; - Experience with numerical simulation methods; - Ability to design problems that mirror real physics research workflows; - Creative thinking in problem design across diverse physics areas; - Familiarity with physics modeling and approximation techniques; - Strong written English (C1+). How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Compensation On this project, contributors can earn up to $76 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.