we are looking for a MLOps Engineer.
As an MLOps Engineer, you will work at the intersection of backend engineering and machine learning, building the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features used by hundreds of thousands of families.
What you'll be doing –
Build Production AI Systems – Design and implement backend services and end-to-end AI solutions that integrate Deep Learning models, Computer Vision algorithms, and GenAI into real features.
Power Experimentation and Validation – Contribute to the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Develop Data Pipelines – Build and maintain scalable data pipelines and big-data solutions that feed AI capabilities reliably and with an eye on cost and scale.
Own What You Ship – Take features end-to-end within your squad, from planning and design through implementation, deployment, and monitoring in production.
Cross-functional Collaboration – Work closely with the Algorithms and Data teams to tackle complex, real-world problems, helping translate research into shippable, maintainable systems.
Backend Guild Engagement – Actively contribute to a backend guild that drives Software Engineering and System Design best practices, guidelines, and standards across the R&D team.
As an MLOps Engineer, you will work at the intersection of backend engineering and machine learning, building the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features used by hundreds of thousands of families.
What you'll be doing –
Build Production AI Systems – Design and implement backend services and end-to-end AI solutions that integrate Deep Learning models, Computer Vision algorithms, and GenAI into real features.
Power Experimentation and Validation – Contribute to the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Develop Data Pipelines – Build and maintain scalable data pipelines and big-data solutions that feed AI capabilities reliably and with an eye on cost and scale.
Own What You Ship – Take features end-to-end within your squad, from planning and design through implementation, deployment, and monitoring in production.
Cross-functional Collaboration – Work closely with the Algorithms and Data teams to tackle complex, real-world problems, helping translate research into shippable, maintainable systems.
Backend Guild Engagement – Actively contribute to a backend guild that drives Software Engineering and System Design best practices, guidelines, and standards across the R&D team.
Requirements:
Professional Experience – 3-5 years of hands-on backend software development experience, demonstrating solid coding skills and a foundational understanding of software design and architecture.
Technical Proficiency –
Production Systems and Cloud – Experience building and operating production-grade services on a cloud platform (AWS preferred), including familiarity with containerization (Docker/Kubernetes), CI/CD, and observability tools like Grafana and Prometheus.
Programming – Strong command of at least one programming language, with Python or Rust being a strong advantage.
Web Services – Proficiency in designing and maintaining web services and APIs, particularly with REST and WebSocket protocols.
AI-Augmented Development – Hands-on experience using AI coding tools in your day-to-day workflow, with genuine curiosity to push their boundaries.
Mindset –
Engineering Quality – A commitment to clean, robust, and rigorously tested code – you treat quality as a first-class engineering concern, not something retrofitted at the end of a sprint.
Self-Learner – A strong ability to self-learn, step out of your comfort zone, and independently take a concept from research to production.
Problem Solver – Strong capability to work through complex issues and adapt to evolving technologies and environments.
Advantages –
Familiarity with the ML model lifecycle – training, evaluation, deployment, and monitoring of models in production.
Experience with Data Engineering and big-data pipelines (e.g., Dagster, Airflow, Iceberg).
Experience with TensorFlow, PyTorch, or Computer Vision concepts.
Experience with distributed systems, message queues (Kafka, RabbitMQ, SQS), and high-scale infrastructure.
Professional Experience – 3-5 years of hands-on backend software development experience, demonstrating solid coding skills and a foundational understanding of software design and architecture.
Technical Proficiency –
Production Systems and Cloud – Experience building and operating production-grade services on a cloud platform (AWS preferred), including familiarity with containerization (Docker/Kubernetes), CI/CD, and observability tools like Grafana and Prometheus.
Programming – Strong command of at least one programming language, with Python or Rust being a strong advantage.
Web Services – Proficiency in designing and maintaining web services and APIs, particularly with REST and WebSocket protocols.
AI-Augmented Development – Hands-on experience using AI coding tools in your day-to-day workflow, with genuine curiosity to push their boundaries.
Mindset –
Engineering Quality – A commitment to clean, robust, and rigorously tested code – you treat quality as a first-class engineering concern, not something retrofitted at the end of a sprint.
Self-Learner – A strong ability to self-learn, step out of your comfort zone, and independently take a concept from research to production.
Problem Solver – Strong capability to work through complex issues and adapt to evolving technologies and environments.
Advantages –
Familiarity with the ML model lifecycle – training, evaluation, deployment, and monitoring of models in production.
Experience with Data Engineering and big-data pipelines (e.g., Dagster, Airflow, Iceberg).
Experience with TensorFlow, PyTorch, or Computer Vision concepts.
Experience with distributed systems, message queues (Kafka, RabbitMQ, SQS), and high-scale infrastructure.
This position is open to all candidates.
