MLOps Team Lead

Join the Market Leader in Electric Power Data and Analytics Solutions

The electrical grid is the largest and most complicated machine ever built. Yes Energy’s industry-leading electric power trading analytics software provides real-time visibility into the massive amount of data generated by the North American electrical grid daily. Our unique and innovative view of the data informs real-time trading decisions and mid-to-long-term investment decisions that keep utility prices low, support the energy transition, and keep the grid running. It’s both challenging work and work with a purpose.

Be a part of our successful, growing business during international transformation.

Position Summary

We are hiring an MLOps Team Lead to build and lead the operational foundation for machine learning, AI, and data science systems across Yes Energy. This role sits within the Platform Technology group and is responsible for making model development, deployment, monitoring, governance, and operations reliable, secure, repeatable, and scalable.

The MLOps Team Lead will be a hands-on technical leader who partners closely with Data Science, Engineering, Product, Security, Data Engineering, and Infrastructure teams. The role will establish MLOps standards, guide platform architecture, mentor engineers, and lead the team responsible for productionizing ML capabilities that support customer-facing products and internal decision systems.

This is a team lead role for someone who can combine strong software engineering and platform engineering fundamentals with practical ML lifecycle experience. Success in this role means creating clear patterns for experimentation, feature management, model deployment, model observability, CI/CD for ML systems, and operational support so Yes Energy can safely deliver data-driven and AI-enabled capabilities at scale.

Position Details

• Salary Range: Net 20.000 – 25.000 RON/month
• Location: Hybrid (Bucharest, Romania)
• Schedule: Full-time; 2-3 days in the office
• Working Hours: 10 AM - 7 PM
• Reporting to: Senior Director of Platform Technology

Primary Responsibilities

• Lead the MLOps function and provide day-to-day technical direction, mentoring, prioritization, and execution support for MLOps engineers and related platform contributors.
• Design, build, and operate scalable MLOps platforms, services, and workflows that support model experimentation, training, validation, deployment, monitoring, and retirement.
• Establish practical standards for model CI/CD, feature pipelines, model registries, artifact management, reproducible training, environment management, and release promotion across development, staging, and production.
• Partner with Data Science teams to turn prototypes into reliable production systems, including batch inference, real-time inference, model APIs, decision services, and data-driven application features.
• Partner with Product leadership to define measurable success criteria for ML-enabled capabilities, including adoption, forecast quality, reliability, cost-to-serve, and post-launch validation.
• Build and improve monitoring for models and ML-powered services, including service health, latency, throughput, data quality, drift, model performance, cost, and operational alerts.
• Create deployment and rollback patterns that make ML releases safe, observable, repeatable, and auditable, including canary releases, shadow deployments, A/B testing support, and model version management where appropriate.
• Collaborate with Data Engineering and Platform teams on reliable feature pipelines, data contracts, orchestration, scheduling, lineage, and dependency management for ML workloads.
• Support cloud-native ML infrastructure across AWS and related environments, including containers, Kubernetes, orchestration tools, storage, networking, IAM, and cost-aware compute patterns for training and inference.
• Partner with Security, Compliance, and Engineering leadership to define guardrails for access control, model governance, auditability, data handling, secrets management, and responsible use of AI-enabled capabilities.
• Drive incident response, operational readiness, runbooks, postmortems, and corrective actions for production ML services and ML platform components.
• Evaluate and introduce fit-for-purpose MLOps tooling while balancing operational simplicity, developer experience, security, cost, and long-term maintainability.

Minimum Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Information Technology, or a related field; or equivalent practical experience.
• Minimum of seven years of professional experience in software engineering, platform engineering, data engineering, ML engineering, SRE, or related technical roles, including at least two years working with production ML, AI, or data science systems.
• Experience leading technical teams or workstreams, including mentoring engineers, setting standards, delegating work, reviewing designs, and driving execution across cross-functional stakeholders.
• Hands-on experience building or operating MLOps workflows such as model training pipelines, model registries, experiment tracking, automated validation, deployment automation, inference services, and model monitoring.
• Strong software engineering skills in Python and modern engineering practices, including version control, automated testing, code review, CI/CD, packaging, and maintainable service design.
• Production experience with cloud platforms, especially AWS, and cloud-native infrastructure such as containers, Kubernetes, orchestration systems, IAM, storage, networking, and monitoring.
• Working knowledge of data pipelines, workflow orchestration, batch and streaming data patterns, data quality checks, and operational dependencies between data systems and ML services.
• Strong communication skills with the ability to translate between data science, engineering, product, security, and executive stakeholders.
• Demonstrated ability to diagnose production issues, coordinate responders, write clear runbooks or postmortems, and drive corrective actions that improve reliability.

Key Competencies & Preferred Qualifications

• Artificial Intelligence: Advanced skill in building, optimizing, and monitoring platforms that scale AI/ML models securely and responsibly.
• Systems Thinking: Takes a holistic view of how data infrastructure, models, applications, and hardware interact across the enterprise.
• Problem Solving: Frames and solves highly complex system challenges, pinpointing root causes and designing durable, scalable infrastructure.
• System Design & Maintenance: Expert ability to engineer highly maintainable, automated, and resilient systems adhering to best practices.
• Security & Compliance: Enforces robust guardrails, data privacy, governance frameworks, and access controls around model pipelines.
• Effective Communication: Translates intricate architectural and operational concepts into clear, actionable priorities for diverse stakeholders.
• Developing People: Committed to coaching, providing actionable feedback, and managing team workloads to foster long-term professional growth.

At Yes Energy, we value connecting directly with candidates. We kindly ask that third-party recruiters and agencies not submit resumes, as we are not open to external recruiting partnerships.

ABOUT YES ENERGY

Overview

Yes Energy delivers real-time market data and electric power trading decision solutions. Over 1,000 market participants use Yes Energy solutions daily. The business is a leader in all aspects of information content collection and management, developing and delivering data and market analytics solutions. Since its inception in 2008, Yes Energy has become a trusted and respected supplier of innovative and reliable solutions focused on the needs of power market analysts, traders, and trade managers. Yes Energy has a team of over 350 amazing professionals in Boulder, CO (HQ); Boston, MA; Chicago, IL; Glendora, CA; Richmond, VA; London, United Kingdom; Auckland, New Zealand; and Bucharest, Romania.

Culture

Yes Energy has been named one of the Best Places to Work in Colorado, and we have the culture to prove it. At Yes Energy, we care about saying “Yes” to customers. We like to listen, learn, and develop our solutions in line with their needs. We think about customers as business partners, and when we help them be more successful … we are more successful, too.

Around the office, our culture is driven by some pretty fundamental values that we’re proud of:

• We love innovation and solving tough challenges;
• We are “high standards people” who combine passion and pride with hard work and rewards of all kinds-- in an ethic that is consistent across the company.
• We’re team-focused with a flat hierarchy-- we work in small teams on well-defined projects that directly impact the success of the business;
• We play to the strengths and experience of each person while each of us also works along a continuum of roles adjacent to our focus area. This presents the challenge of maintaining a broad set of skills as well as an opportunity to learn and contribute in many ways.
• We are constantly growing. Professional development happens every day and every year.

Compensation and Benefits

We offer highly competitive salaries and real bonuses that are achievable and that you can impact. Our benefits package includes private medical insurance, wellness/gym benefits, flexible vacation, and flexible work schedules. Yes Energy encourages and funds investment in both formal and informal professional development.

Yes Energy provides equal employment opportunities to all employees and applicants without regard to race, color, religion, sex, national origin, age, disability, genetics, or any other protected status. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, compensation, and training.