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We are STX Next, a global IT consulting company specializing in customer-focused software services. Join a group of 500 professionals dedicated to helping customers build outstanding products. Leveraging the latest advancements in the field, and a passion for innovation, we're shaping the future of technology one project at a time.
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Job Description
At STX Next, we build ML solutions that go into real production use: from classical tabular modelling, through vision computing and predictive maintenance, to ML systems enhanced with LLMs. We’re looking for an ML Engineer who can handle the entire process — from data work and modelling to deployment. We work in a remote-only model, in a team of experienced engineers where autonomy, technical reliability, and responsible decision-making matter.
Responsibilities
- Building ML models: classification, regression, clustering, tabular ML.
- Working with data: preparation, analysis, feature engineering, validation.
- Projects involving vision computing, predictive maintenance, and time series.
- Implementing models as APIs and integrating them with client systems.
- Designing ML architectures end-to-end.
- 10–20% research — as needed, but the job is primarily delivery.
- Opportunity to expand into LLM/RAG, if desired.
- Production-level Python (clean code, typing, tests, solid architecture).
- At least one commercially deployed ML project.
- Strong knowledge of:
- Pandas, Numpy,
- scikit-learn,
- at least one DL framework (PyTorch or TensorFlow).
- Ability to run the full ML lifecycle: Data analysis → Modelling → Deployment → Monitoring basic metrics.
- SQL knowledge sufficient for analytics and basic transformations.
- Basic familiarity with cloud (AWS/GCP/Azure).
- Experience with CV or time series forecasting.
- Basic MLOps (Docker, MLflow, Airflow, DVC).
- Basic LLM/RAG knowledge.
- Vector databases, HF, Wandb.
- You work independently and close projects reliably.
- You can design ML architectures.
- You’re not afraid to ask questions or consult when needed.
- You prefer building models that reach production instead of sitting in “experiments”.
- You choose pragmatic solutions over unnecessary complexity.
- Work-life Balance
- Reimbursed private medical care (Medicover) and Multisport
- Leader’s support
- Technology focus
- Growth review
- Events
- Workation
Key Skills
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