Applied AI Engineer

We’re looking for a highly skilled Applied AI/Machine Learning Engineer who can bridge the worlds of data science, data engineering, and AI deployment. You’ll be responsible for building end‑to‑end machine learning solutions — from collecting and preparing data, to training, evaluating, and deploying models that enhance the Kammelna Games player experience (e.g., churn prediction, personalization, user engagement optimization).This role is ideal for someone who thrives at the intersection of hands‑on data work, machine learning model development, and scalable AI systems.

Responsibilities

• Design, develop, and deploy machine learning models for real‑world use cases (player behavior prediction, personalization, retention modeling, fraud detection, etc.).
• Build and maintain data pipelines (ETL/ELT) to collect, clean, and process large datasets from multiple sources (game analytics, APIs, user data).
• Collaborate with product and engineering teams to integrate AI models into production environments (APIs, backend systems).
• Implement and manage MLOps practices — model versioning, CI/CD for ML, monitoring, and retraining pipelines.
• Conduct exploratory data analysis (EDA) to uncover insights and guide model features.
• Optimize models for accuracy, latency, and scalability.
• Develop dashboards and reports to communicate model performance and insights to non‑technical stakeholders.
• Stay current with advances in ML/AI frameworks (LLMs, deep learning, reinforcement learning) and proactively propose innovative ideas.

Core Technical Skills

• Strong programming in Python (pandas, NumPy, scikit‑learn, TensorFlow or PyTorch).
• Solid understanding of machine learning algorithms (supervised, unsupervised, deep learning).
• Hands‑on experience with data engineering tools:
  • ETL frameworks (Airflow, Prefect, or custom pipelines)
  • SQL and NoSQL databases (PostgreSQL, BigQuery, MongoDB)
  • Data storage (S3, GCS, etc.)
• Experience with MLOps / production deployment:
  • Containerization (Docker, Kubernetes)
  • APIs (FastAPI, Flask)
  • CI/CD for ML (MLflow, Kubeflow, or Vertex AI)
• Familiarity with cloud platforms (AWS, GCP, or Azure ML).
• Proficiency in version control (Git) and collaborative workflows.

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• 3–7 years of hands‑on experience in machine learning, data science, or AI engineering.
• Proven experience delivering end‑to‑end ML projects from concept to deployment.

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