Evolution.inc

Evolution is an AI platform that automates complex data science problems by building AI systems. It uses AI4AI technology with parallel genetic algorithms running on GPUs to automatically generate…

AIAI Evolving AIAI-for-AIAI OptimizationAI ServicesAlgorithmsBig DataDataData MiningData ScienceDeep LearningGenetic AlgorithmsMachine Learningevolution.inc

Evolution.inc

Evolution is an AI platform that automates complex data science problems by building AI systems. It uses AI4AI technology with parallel genetic algorithms running on GPUs to automatically generate…

AIAI Evolving AIAI-for-AIAI OptimizationAI ServicesAlgorithmsBig DataDataData MiningData ScienceDeep LearningGenetic AlgorithmsMachine Learningevolution.inc

HQRamat Gan

Team Size13

Open JobsUnknown

Total Funding-

Latest FundraiseUnknown

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MLOps Engineer

On-SiteRamat Gan, Tel Aviv District

On-Site • Ramat Gan, Tel Aviv District

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MLOps Engineer Position at Evolution

About the company:

At Evolution, we’re building AI that builds AI (AI-for-AI). Our platform, Earth, autonomously designs and improves AI systems. It’s powered by a cutting-edge fusion of modernized evolutionary algorithms (genetic algorithms) and state-of-the-art AI techniques, purpose-built for today’s most demanding challenges in ML and AI.

What makes Evolution unique?

Evolution operates across a wide spectrum of AI domains — including prediction, LLMs, computer vision, signal processing, generative AI, complex planning, real-time control, and more. This breadth allows for a dynamic and intellectually stimulating environment, where each project brings new scientific challenges and opportunities. No two days are the same, and team members regularly engage with a variety of methods, tools, and problem spaces.

Job description:

As an MLOps Engineer at Evolution, you’ll be responsible for advancing the infrastructure, tooling, and operational backbone that power our AI-for-AI platform, Earth, as well as multidisciplinary real-world, high-impact data science implementations. This role sits at the intersection of software engineering, cloud architecture, and ML/AI. You’ll be working with a team of brilliant and experienced data scientists, and working closely with product and business stakeholders to align goals and priorities. You will take ownership of designing and maintaining scalable, production-grade systems that enable rapid experimentation, seamless deployment, and robust execution of advanced AI workflows. This position will require undergoing a security clearance process.

What you’ll do:

Mandatory requirements:

BSc / BA degree in Computer Science or Software Engineering.
3+ years of hands-on experience with Python, including writing maintainable and production-grade code.
Practical experience with Docker for containerization and Kubernetes for orchestration in production environments.
Strong practical knowledge of AWS cloud services.
Hands-on experience building and supporting production data/ML pipelines, including: Interaction with S3 datasets; Orchestrating flows using Lambda, Step Functions and EventBridge; Managing scheduled or event-driven processes.

Advantage:

MSc / MA degree in Computer Science or Software Engineering.
Experience with CI/CD tools and workflows (GitHub Actions, GitLab CI, CodePipeline).
Experience implementing task orchestration or background processing using TaskIQ (or similar frameworks).
Strong knowledge of asynchronous programming and event-driven service design.
Native-level English or strong command of written and spoken English.
Demonstrated strength in technical writing and documentation.

Job location:

Our office is located in the Bursa district of Ramat-Gan, right next to Israel Railways and the Light Rail. This is a primarily on-site role, with most work done from the office.

Contribute to the evolution of our own AI-for-AI platform (Earth) by developing new capabilities, optimizing execution flows, and building tooling that accelerates research.

Foster a fast-paced, Agile development culture and contribute to the product roadmap.

Collaborate with product and business teams to align on goals and KPIs.

Engage directly with clients: from translating business needs to providing support.

Deploy, integrate and monitor production-level data and ML pipelines.

Design and implement cloud-native architectures on AWS, ensuring scalability, robustness, and cost-efficiency.

Build and maintain containerized services using Docker and Kubernetes, including cluster-level orchestration and automation.

Develop backend and infra services using Python and FastAPI, with clean, maintainable, and production-grade code.

Manage and model data across S3, SQL, and NoSQL systems; implement efficient ODM/ORM-based storage flows (Beanie or equivalent).

Implement event-driven and scheduled workflows using Lambda, Step Functions, EventBridge, or similar orchestration tools.

Enable data-science teams with reproducible environments, infrastructure abstractions, and seamless integration with backend services.

Improve reliability and observability across pipelines and services using logging, monitoring, and alerting best practices.

Proven experience building and maintaining services using FastAPI.

Strong familiarity with Beanie (or similar ODMs) for data modeling and persistence.

Experience with SQL and NoSQL databases, designing schemas, queries, and performance tuning.

Ability to support data science teams with infrastructure, reproducible environments, and integration with backend services.