Senior GenAI + Agentic Data Scientist - Hybrid Lisbon (1 day/week on-site)

ABOUT THE OPPORTUNITY

Join a leading gaming and entertainment technology company at the forefront of AI innovation as they build a brand-new Generative AI team from the ground up. Two positions available for exceptional Data Scientists with proven GenAI and Agentic AI expertise to shape the future of AI-powered products serving millions of users across gaming, sports betting, and entertainment platforms. This is a rare opportunity to be a founding member of a strategic AI initiative, working with cutting-edge Large Language Models, autonomous agents, and advanced machine learning systems that will transform how the business operates. Operating with exceptional flexibility requiring only 1 day per week in the Lisbon office, you'll work alongside world-class engineers building production-grade AI applications. This role combines deep technical expertise in machine learning with hands-on GenAI development, offering the chance to translate product requirements into breakthrough AI solutions while mentoring team members and driving complex projects from conception to production deployment.

PROJECT & CONTEXT

You'll be at the forefront of building AI-powered products from scratch as part of a newly formed Generative AI team, analyzing data, developing sophisticated machine learning models, and working closely with the tech organization to create successful AI applications. Critical to this role is hands-on experience with Generative AI and Agentic AI systems—building applications with LLMs, designing autonomous agents, implementing RAG architectures, and deploying GenAI solutions to production. Your responsibilities span translating product requirements into machine learning problems, identifying areas where AI delivers maximum business impact, and managing the full lifecycle of AI features from data collection through model design to production optimization. You'll perform exploratory data analysis (EDA) and feature engineering, apply best practices in model selection and parameter tuning, run comparative experiments for model training, and analyze ML metrics to evaluate solutions. Working with Python's machine learning ecosystem, you'll leverage deep learning frameworks, develop MLOps solutions ensuring reliable model deployment and monitoring, and implement production-grade systems handling scale across gaming, fraud detection, CRM, and sportsbook domains. The position requires mentoring junior team members, sharing knowledge across the organization, and leading complex AI projects that push the boundaries of what's possible with Generative AI and autonomous agent systems.

WHAT WE'RE LOOKING FOR (Required)

Machine Learning Expertise: Expert knowledge of machine learning algorithms and underlying theory with deep understanding of various ML paradigms

Production ML Experience: Extensive hands-on track record delivering machine learning models to production environments at scale

GenAI Experience: MANDATORY practical experience developing Generative AI applications including LLMs, prompt engineering, and GenAI architectures

Agentic AI Experience: MANDATORY hands-on experience building Agentic AI systems, autonomous agents, and multi-agent workflows

MLOps Development: Proven experience developing MLOps solutions for model deployment, monitoring, versioning, and lifecycle management

Python ML Ecosystem: Deep knowledge of Python machine learning libraries (scikit-learn, TensorFlow, PyTorch, Keras, pandas, numpy)

Deep Learning: Strong understanding of deep learning architectures, neural networks, and modern DL frameworks

Software Engineering: Solid software development background with Object-Oriented Programming (OOP) principles

Full ML Lifecycle: Managing complete AI feature lifecycle from data collection to production optimization

Exploratory Analysis: Expertise in exploratory data analysis (EDA), feature engineering, and data preparation

Model Development: Best practices in model selection, hyperparameter tuning, and experiment design

ML Metrics: Strong analytical skills evaluating models using appropriate metrics and validation techniques

Teamwork Excellence: Strong collaboration skills working with cross-functional teams including engineering, product, and business stakeholders

Communication: Exceptional written and verbal communication abilities for technical and non-technical audiences

Analytical Thinking: Sharp analytical and problem-solving mindset for complex AI challenges

Mentorship: Ability to mentor and guide junior team members, sharing knowledge and best practices

Project Leadership: Experience leading complex machine learning projects from conception to delivery

Language: B2 English (Upper Intermediate) minimum - fluent oral and written communication

Location: Based in Portugal with availability for 1 day per week in Lisbon office

NICE TO HAVE (Preferred)

Big Data Tools: Experience with Apache Spark and PySpark for large-scale data processing

Cloud Platforms: Hands-on experience with cloud environments, ideally Azure and Databricks

Recommendation Systems: Building and deploying recommendation engines and personalization systems

LLM Frameworks: Experience with LangChain, LlamaIndex, or similar GenAI development frameworks

RAG Architectures: Implementing Retrieval-Augmented Generation for knowledge-grounded AI applications

Vector Databases: Working with Pinecone, Chroma, Weaviate, or similar vector storage solutions

Agent Frameworks: Experience with AutoGen, CrewAI, or other multi-agent orchestration platforms

Fine-Tuning: LLM fine-tuning experience using LoRA, QLoRA, or full parameter training

Domain Experience: Relevant industry background in fraud detection, CRM systems, sportsbook/gaming, or iGaming

Azure ML: Azure Machine Learning platform for model training and deployment

MLflow: Experiment tracking, model registry, and deployment with MLflow

A/B Testing: Designing and analyzing A/B tests for ML model evaluation

Feature Stores: Experience with feature engineering platforms (Feast, Tecton)

Model Monitoring: Implementing drift detection, performance monitoring, and model observability

Distributed Training: Training large models across multiple GPUs or distributed systems

NLP Expertise: Natural Language Processing and text analytics background

Computer Vision: Image processing and computer vision model development

Reinforcement Learning: RL algorithms and applications in gaming or optimization

Leadership Experience: Previous experience leading AI/ML teams or acting as technical lead

Location: Lisbon, Portugal (Hybrid - 1 day/week on-site)