MLOps Team Lead

About the Client

Our client is an innovative software company focused on transforming complex, high-volume data into clear, actionable insights. Its platform empowers organizations to make informed decisions through real-time analytics, forecasting, and operational intelligence.

The company develops cloud-native solutions for large-scale data processing, streaming, analytics, and decision support, handling over one billion data updates per day from over 1000+ data sources daily.

The Role

Responsibilities

Provide technical leadership, mentoring, prioritization, and day-to-day guidance for MLOps engineers and platform contributors.
Design, implement, and maintain scalable MLOps platforms supporting model development, training, validation, deployment, monitoring, and retirement.
Define standards for ML CI/CD, model registries, artifact storage, feature management, reproducible training environments, and release promotion processes.
Collaborate with Data Science teams to convert experimental models into reliable production services, including batch and real-time inference solutions.
Work with Product stakeholders to establish measurable outcomes for ML-driven capabilities and assess post-release performance.
Implement comprehensive monitoring covering service health, latency, throughput, model quality, data quality, drift, operational costs, and alerting.
Develop safe deployment strategies, including rollback mechanisms, canary releases, shadow deployments, A/B testing approaches, and model version control.
Partner with data and platform teams to support feature pipelines, orchestration, scheduling, lineage, data contracts, and workload dependencies.
Support cloud-native infrastructure across AWS environments, including Kubernetes, containers, networking, storage, IAM, and compute optimization.
Establish governance, security, auditability, access management, secrets handling, and responsible AI controls for ML systems.
Lead operational readiness activities, incident response processes, runbook creation, post-incident reviews, and corrective actions.
Assess and introduce MLOps tooling while balancing reliability, developer experience, security, cost, and maintainability.

Must-haves

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Information Technology, or a related discipline, or equivalent practical experience.
Minimum seven years of experience in software engineering, platform engineering, data engineering, ML engineering, SRE, or similar technical roles.
At least two years of experience supporting production ML, AI, or data science workloads.
Previous experience leading technical teams, coordinating workstreams, mentoring engineers, reviewing designs, and driving execution.
Hands-on expertise with model training pipelines, experiment tracking, model registries, deployment automation, inference services, validation frameworks, and model monitoring.
Strong software engineering skills in Python, including version control, automated testing, code reviews, packaging practices, CI/CD, and maintainable service development.
Practical experience with AWS and cloud-native technologies such as Kubernetes, containers, IAM, networking, storage platforms, orchestration systems, and monitoring solutions.
Knowledge of batch and streaming data architectures, workflow orchestration, data quality controls, and dependencies between data and ML systems.
Experience investigating production issues, coordinating response efforts, documenting runbooks, and implementing long-term reliability improvements.
Strong communication skills with the ability to collaborate effectively across engineering, data science, product, security, and leadership teams.

Nice-to-haves

Advanced expertise in building, optimizing, and operating AI/ML platforms at scale.
Strong systems-thinking approach across applications, infrastructure, data platforms, and ML ecosystems.
Proven ability to solve complex platform and infrastructure challenges through scalable design practices.
Experience implementing security, privacy, governance, and compliance controls for ML environments.
Demonstrated capability to coach and develop engineering talent while managing workloads and delivery priorities.

Benefits

Performance bonus of 10% to 15% of annual base salary
Flexible time-off policy with untracked vacation days

Selection Process

Interview Process & How to Prepare

Screening Interview (with Hiring Manager) Focus: culture fit, background, and your motivation. Be ready to walk through your CV and ask questions about the company.
Panel Interview (mixed US/RO panel, evening slot 17:00-18:00 start, ~2 hours)

Hour 1: Behavioral round covering conflict resolution and ownership of responsibilities. Prepare concrete examples from your own experience (STAR format works well).

Hour 2: Technical round. You will be discussing an MLOps system design. In the interview, you'll walk the panel through your approach and justify your choices.

Good to Know

Come prepared with real examples from your experience rather than hypothetical scenarios. During discussions, focus on explaining your thought process, key decisions, challenges encountered, trade-offs considered, and the impact of your work.

MLOps Team Lead

About the Client

The Role

Responsibilities

Must-haves

Nice-to-haves

Benefits

Selection Process

Good to Know

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