Machine Learning Engineer

We are looking for a Machine Learning Engineer to strengthen the engineering backbone of our ML systems - designing robust training pipelines, deploying models to production, and ensuring our solutions run reliably at scale on cloud infrastructure.

Work Mode

Fully remote. On-site collaboration 2-3 days per quarter.

Key Responsibilities

* ML Pipeline Development: Design, build, and maintain end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, evaluation, and serving.
* Model Training & Optimization: Train and optimize gradient boosting models (LightGBM, XGBoost) and deep learning architectures such as Temporal Fusion Transformers (TFT) for time-series forecasting at scale.
* Cloud Infrastructure: Deploy and manage ML workloads on AWS (S3, EC2, Lambda), including containerized training jobs, scheduled retraining, and model artifact management.
* Production Deployment: Package models for production use with proper versioning, monitoring, and automated testing. Ensure reproducibility and traceability of experiments.
* Code Quality & Best Practices: Write clean, well-tested, modular Python code following software engineering best practices (OOP, design patterns, typing, linting, CI/CD).
* Collaboration: Work closely with Data Scientists and business stakeholders to translate analytical prototypes into production-ready solutions and communicate technical decisions clearly.

You Must Have

* A degree in Computer Science, Data Science, Engineering, Applied Mathematics, or a related field.
* Minimum 2 years of commercial experience as a Machine Learning Engineer, Data Engineer, or a similar role with strong ML focus.
* Strong Python skills with emphasis on best practices (OOP, type hints, testing, clean architecture, packaging).
* Hands-on experience training and deploying gradient boosting models (LightGBM, XGBoost).
* Working knowledge of AWS (S3, EC2, Lambda) and the ability to set up and manage cloud-based ML workloads.
* Proficiency in Docker for containerizing ML services and pipelines.
* Solid SQL skills for data extraction and transformation.
* Comfortable working in a Linux terminal environment.
* Communicative English, sufficient for reading documentation, code reviews, and presenting technical decisions to the team.

Nice to Have

* Experience with experiment tracking and model registry tools (e.g. MLflow).
* Experience with OCR tools
* Experience with deep learning models for time-series, in particular Temporal Fusion Transformers (TFT) or similar architectures.
* Experience with workflow orchestration (e.g. Airflow, Prefect, Step Functions).
* Familiarity with hyperparameter optimization frameworks (e.g. Optuna).
* Knowledge of Infrastructure as Code (e.g. Terraform, CloudFormation).
* Familiarity with CPG/FMCG or Retail data domains.
* Experience with Explainable AI tools (e.g. SHAP).

What We Offer

* Choice of employment contract or B2B.
* Fully remote work with quarterly on-site meetups (2-3 days).
* Work with large-scale datasets and real impact on decisions of major corporations.
* A structured development process from research through to production deployment.
* Access to cloud computing infrastructure (AWS) and a modern technology stack.
* Opportunity to shape the ML engineering culture and tooling within the team.
* Mentorship support.
* Multisport card.
* Private medical care.

PSignite invests in the development of its employees. We are committed and aspire to leverage the qualities and appreciate each person's unique competencies to bring to our company. We are an Equal Opportunity, Affirmative Action employer. Minorities, women, veterans, and individuals with disabilities are encouraged to apply.