AI and ML Engineer

Technologies we use

Expected

• Python
• TensorFlow
• PyTorch
• scikit-learn
• Git
• AWS
• Apache Spark
• Kubernetes

Optional

• Snowflake Data Cloud
• Databricks
• Microsoft Azure

About the project

We are helping to develop a decision support system for investment analysts.

The project includes development, optimization, and speeding up applications and databases.

DataArt specialists work on the project in several areas related to the core business of the client.

This is how we organize our work

Team size

10-20

This is how we work

• you develop several projects simultaneously
• you have influence on the technological solutions applied
• you focus on product development

Team members

• backend developer
• technical leader
• data scientist

Your responsibilities

• Design, implement, and optimize machine learning and AI models, including classification, regression, clustering, and agent tuning.
• Experiment with the latest methods, frameworks, and architectures to enhance model performance and efficiency.
• Develop robust, scalable ML pipelines for training, validation, and inference.
• Deploy ML models into production environments (cloud or on-prem), ensuring high reliability, low latency, and scalability.
• Apply MLOps best practices including CI/CD, monitoring, automated retraining, and model registry management.
• Partner with data engineering teams to source, clean, and transform large datasets for model training and inference.
• Ensure high data quality, perform feature engineering, and support real-time data integration processes.
• Work closely with data scientists, software engineers, and product managers to align ML solutions with business goals.
• Clearly communicate complex technical results to both technical and non-technical stakeholders.
• Provide technical guidance and mentorship to junior ML engineers and data scientists.
• Contribute to establishing team best practices, code reviews, and architectural decisions.

Our requirements

• 5+ years of professional experience in machine learning, AI engineering, or related fields.
• Master’s degree in Computer Science, Machine Learning, Physics, or related field; PhD preferred.
• Proficient in Python and ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Strong understanding of algorithms, statistics, probability, and linear algebra.
• Hands-on experience with data pipelines and ETL tools (e.g., Spark, AWS Lambda, AWS Glue).
• Practical experience with cloud platforms, preferably AWS.
• Solid software engineering fundamentals including version control (Git), testing, and design patterns.
• Demonstrated success in deploying ML models into production at scale.
• Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI.
• Excellent analytical, problem-solving, and communication skills.
• Ability to work autonomously and collaboratively within a fast-paced, cross-functional team environment.

Optional

• Experience with agentic frameworks like LangChain or LangGraph

What we offer

• This is a contract position.

This is how we work on a project

• code review
• Continuous Deployment
• Continuous Integration
• documentation
• test automation

Development opportunities we offer

• development budget
• external training
• intracompany training
• mentoring
• support of IT events
• technical knowledge exchange within the company

Benefits

• sharing the costs of sports activities
• private medical care
• sharing the costs of foreign language classes
• sharing the costs of professional training & courses
• life insurance
• remote work opportunities
• flexible working time
• corporate products and services at discounted prices
• integration events
• coffee / tea
• drinks
• sharing the costs of tickets to the movies, theater
• employee referral program
• opportunity to obtain permits and licenses
• extra leave

DataArt Poland sp. z o.o.

Our client is one of the world's largest investment funds that manages hundreds of billions of dollars.

The company's business is focused on several areas: private equity, venture capital, real assets, investment solutions, and global market strategies.