Senior MLOps Platform Engineer.

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Armenia, Georgia, Ukraine

Remote

Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

About the Product:

We are building an AI-first platform that fuses multiple maritime data sources into a unified operational picture. The system builds a digital twin of global maritime activity, applies behavioral analytics and predictive AI models to surface smuggling, illegal fishing, container risk, and other compliance or security threats, and presents mission-grade intelligence to both commercial and government stakeholders.

Operating at global scale, the platform processes millions of events and terabytes of geospatial data daily, relies on cloud infrastructure and microservices, and integrates with upstream research prototypes from data science teams.

About the Role:

As a Senior MLOps Platform Engineer, you will be the driving force behind scaling AI capabilities by owning end-to-end infrastructure and processes supporting the full lifecycle of ML models. You will collaborate closely with data scientists, data analysts, and platform engineers to design and build systems for dataset and label management, model registry, experiment tracking, and model serving.

Your contributions will enable faster, more reliable model deployments and deliver measurable impact in a mission-critical AI platform.

Key Responsibilities:

Design and implement systems for dataset and label management, including versioning and customer feedback integration.
Establish and maintain a model repository/registry with version control, lineage tracking, and local inference support.
Lead implementation of experiment tracking and monitoring solutions for data science and generative AI, focusing on evaluation, drift detection, and reproducibility.
Lead the deployment, lifecycle management, and continuous improvement of ML/DL models in production.
Own model serving and inference infrastructure, including autoscaling, A/B testing, canary deployments, and latency/cost optimization.
Enable generative AI capabilities by developing tagging tools, prompt management services, and LLM testing frameworks.
Drive operational excellence by improving tool deployment usability and establishing granular cost visibility across environments and projects.
Develop reusable components such as standardized data loaders, CI/CD pipelines, and automated model retraining workflows.
Collaborate with data scientists and platform engineers to productionize ML/DL models in public cloud environments.

Required Competence and Skills:

At least 4+ years of hands-on experience in MLOps / ML Platform Engineering (or equivalent ML Engineering experience with strong MLOps ownership). Able to independently design, build, and scale production ML infrastructure.
Experience collaborating closely with Data Scientists or ML Research teams to productionize machine learning models.
Strong Python programming skills.
Hands-on experience in containerization, CI/CD, and public cloud platforms (AWS, Azure, or GCP) for deploying, serving, and monitoring ML models.
Proficiency with dataset management, model versioning, experiment tracking, monitoring, and MLOps platforms (e.g. MLflow, SageMaker, or similar tools).
Experience with machine learning frameworks (PyTorch or TensorFlow), big-data technologies (Apache Spark), and data stores such as PostgreSQL, MongoDB, and Redis.
Experience using AI-assisted development tools and adopting Generative AI and agentic workflows in day-to-day software development.
B.Sc. or M.Sc. in Computer Science, Software Engineering, or a related field.