MLOps Engineer - Remote

Level

Senior

Department

Type

Full Time

Project

NDA

Locations:

Armenia

Remote

Job Details

Posted on:

July 3, 2026

Job ID:

KhJdS5

About the Company

Established in 2004, ALLSTARSIT was founded with a clear vision: to enhance the landscape of global IT employment by bridging the gap between companies and skilled professionals. The core belief was that assembling a team shouldn't be hindered by geographical constraints. Fast forward to the present day, ALLSTARSIT stands as an international outstaffing service provider committed to change the way businesses recruit, compensate, and oversee top talent worldwide.

With operational hubs scattered across Europe, Asia, and LATAM, and its headquarters situated in San Francisco, US, the company boasts a workforce of over 1,000 adept professionals. Spanning across more than 20 countries, ALLSTARSIT offers a diverse range of skilled employees across various verticals, including AI, cybersecurity, healthcare, fintech, telecom, media, and so on.

About the Project

You will be part of a research team, directly impacting the fundamentals of our data research, supporting the creation and improvement of our core-data algorithms.
As part of your role, you will be the driving force to scale our AI capabilities.
You will own the infrastructure and processes that support the full lifecycle of AI models - from research through production and performance monitoring.

Specialization

Headquarters

Years on the market

Team size and structure

Current technology stack

Required skills:

B.Sc. or M.Sc. in Computer Science or Software Engineering or related field
Experienced with ML/DL workflows and their best practices
Experienced with CI/CD workflows and their best practices
Worked with public cloud (AWS/Azure/GCP)
Experienced with Python and Java
Experience with various data stores like Postgres, MongoDB, Redis
Experience with DS tools such as MLFlow, Langfuse, SageMaker, etc.
Experience with Spark
Experience with PyTorch/TensorFlow

Scope of work:

Lead and drive the deployment, lifecycle management, and monitoring of ML/DL models in all stages leading to production.
Design and implement systems for Dataset and Label Management, including versioning and integrating customer feedback into labeling workflows.
Establish and maintain a robust Model Repository/Registry that supports versioning, local inference, and model lineage.
Lead the implementation of advanced Experiment Tracking and Monitoring solutions for both Data Science and Generative AI, focusing on evaluation, data drift detection, and model reproducibility.
Own model serving and inference systems—including autoscaling, A/B testing, canary rollouts, and latency/cost optimization for production models.
Enable specialized infrastructure for Generative AI capabilities, including tagging tools, prompt management, and LLM testing services.
Drive operational excellence by improving tool deployment usability and implementing granular cost visibility across projects and environments.
Developing reusable components such as standardized data loaders, CI/CD pipelines, and automated workflows for tasks like model retraining.
Collaborate directly with Data Scientists and the rest of the Data Platform Engineering team to productionize ML/DL models developed for cloud environments.