Machine Learning / AI Engineer

Company: SPOTIO

Location: Poland (R&D Team), Gdańsk

Type of work: Hybrid (2 days a week from the CUBE office)

Department: Engineering

Salary: 25.000 - 30.000 PLN, B2B / month / full-time

Contact person: Agnieszka Myśliwczyk

A Little About Us

SPOTIO is a dynamic, fast-growing American start-up with a 10-year tradition of creating the #1 Sales Engagement Platform. SPOTIO's platform helps field sales teams manage sales activities, increase the productivity of sales representatives and record field sales insights.

We have offices in Dallas, Texas and Gdansk. In Gdansk, there is a development, product and QA team totalling 21 people (50 people in total globally).

How do we work?

There's no corporate vibe or dress code here. Instead, there's a friendly, collaborative atmosphere and small teams. On Tuesdays and Wednesdays, we meet in the office in Gdansk because we want to. On other days, we work remotely because we can.

We have calls with the Dallas team several times a week, typically only during overlapping working hours of 2:00pm to 5:00pm Polish time. There is also an opportunity for travel to Dallas, however this is not required.

No expected overtime.

About The Role

As SPOTIO's AI/ML Engineer, you will take full ownership of the direction and execution of our machine learning and AI initiatives. You will maintain our existing production models and infrastructure while architecting new ML systems that power intelligent recommendations, predictive scoring, and AI-driven workflows across the entire SPOTIO platform.

You will own the end-to-end ML lifecycle: feature engineering, model training and evaluation, production deployment, drift monitoring, and retraining operations. You will also work closely with our engineering and product teams to integrate ML outputs into our LLM layer, shape the AI descriptions and recommendations that reps see in the product, and potentially fine-tune large language models as that capability matures.

Tech stack you'll work with

ML and Data Science

Python, LightGBM, SHAP, Thompson Sampling, feature engineering, model registries, LLM integration and fine-tuning

Cloud and Infrastructure

Microsoft Azure (SQL, Cosmos DB, Blob Storage, ADLS Gen2, Event Hubs, AKS, Container Apps, Stream Analytics)

API and Services

FastAPI, REST APIs, Managed Identity / API key auth, VNet-internal services

Data and Storage

Azure SQL (multi-zone), Cosmos DB (MongoDB API), Elasticsearch, Redis, Azure Blob / ADLS Gen2

DevOps and Monitoring

Azure DevOps Pipelines, Argo CD (planned), Application Insights, Azure Monitor, Docker, Kubernetes

Your main responsibilities

• Own the full ML lifecycle for SPOTIO's AI features: problem framing, feature engineering, model training and evaluation, production deployment, and ongoing retraining as new data accumulates.
• Operate and evolve the production scoring service, a containerized REST API running on Azure that serves real-time predictions across multiple ML capabilities for all active SPOTIO customers.
• Monitor model health across all companies and capabilities: track retrospective accuracy metrics, feature drift statistics, nightly batch success rates, and API latency through SPOTIO's observability stack.
• Manage the model retraining cycle, including assembling training datasets, evaluating candidate models against quality gates, and promoting new versions through the model registry.
• Triage and resolve production scoring failures, including upstream data access issues, feature extraction errors, and batch pipeline anomalies.
• Maintain and extend the feature engineering pipeline as SPOTIO's data model and customer base grow, including handling per-company custom field classification and multi-zone Azure SQL routing.
• Manage ML feedback loops, including reinforcement-style approaches such as Thompson Sampling bandits, to incorporate user behavior signal into model training over time.
• Collaborate with SPOTIO engineering to integrate ML outputs into the LLM-powered AI layer, shaping the AI-generated descriptions and next-best-action recommendations that sales reps see in the product.
• Explore and evaluate opportunities for LLM fine-tuning as SPOTIO's AI capabilities expand.
• Work with product and engineering on the design and activation of new AI features beyond the current scoring suite.
• Contribute to SDLC ceremonies: architecture design, code review, CI/CD, and agile planning.
• Maintain the operational runbook and produce production monitoring reports on observed model behavior.
  
  

Requirements & Skills

What we expect from you:

• At least 5 years of professional experience in ML engineering, applied data science, or a closely related role.
• Strong Python skills, with experience building and deploying production ML services (FastAPI or equivalent).
• Hands-on experience training, evaluating, and deploying machine learning models across a range of approaches, with particular depth in gradient boosting methods (LightGBM, XGBoost, or similar).
• Experience with model explainability tools, particularly SHAP.
• Experience with Pytorch, SKLearn, Numpy, Pandas / Polars and similar training libraries.
• Solid Cloud Computing experience: Azure, AWS, or GCP.
• Proficiency in SQL (T-SQL or equivalent) with an understanding of query patterns, connection pooling, and performance considerations in high-volume environments.
• Working knowledge of MLOps practices: model registries, versioned artifacts, drift monitoring, and automated retraining pipelines.
• Familiarity with LLMs and experience integrating ML outputs into LLM-driven product features; exposure to fine-tuning workflows is a plus.
• Familiarity with event-driven and distributed system architectures.
• Precision, proactivity, and comfort taking full ownership of systems.
• Proficiency in English.
• Education related to computer science, mathematics, statistics, or a related field.
  
  

Nice to have

• Experience with multi-armed bandits or reinforcement learning approaches.
• Familiarity with Elasticsearch.
• Azure Event Hubs, Stream Analytics, or ADLS Gen2.
• Docker and Kubernetes.
• Azure DevOps Pipelines or Argo CD.
• Exposure to sales CRM data, territory management, or field sales workflows.
  
  

Recruitment Process

• HR interview
• Technical interview with a Backend Team, 1-1,5h
• Meeting CTO, 1h
• Meeting in the office 🙌
  
  

Contact person: Agnieszka Myśliwczyk