We are seeking an experienced Data Science / Machine Learning Engineering Lead to join our team and drive the development of advanced ML/AI capabilities. You will lead a team of Data Scientists / ML Engineers, focusing on building and deploying cutting-edge machine learning solutions using our modern ML infrastructure including Anthropic, OpenAI, and self-hosted LLMs.
- Team Leadership & Management
- Lead, mentor, and develop a team Data Scientists, Data Engineers, ML Engineers
- Conduct regular 1:1s, performance reviews, and career development planning
- Foster a collaborative, innovative team culture focused on continuous learning
- Coordinate work allocation and ensure timely delivery of projects
- Facilitate knowledge sharing and best practices across the team

- Technical Leadership
- Design and implement scalable ML model training pipelines using modern toolset (e.g MLflow, Comet, Langfuse, WandB, Trino, dbt, Spark, Flink, etc)
- Lead fine-tuning initiatives for both commercial (Anthropic Claude, OpenAI GPT) and open-source LLMs
- Utilise self-hosted LLM infrastructure using Ray, AIBrix, and vLLM for optimal performance and cost efficiency with Lora/QLora
- Architect and oversee model continous validation frameworks within our ecosystem
- Develop real-time anomaly detection systems leveraging for streaming data processing
- Build predictive models for system performance, usage patterns, and automation workflow optimization
- Establish ML engineering best practices for model versioning, monitoring, and deployment on Kubernetes
- Creation of eval, validation and metrics pipelines for models during training and inference

- Strategic Initiatives
- Optimize the balance between commercial APIs (Anthropic, OpenAI) and self-hosted models for different use cases
- Partner with product and engineering teams to identify high-impact ML opportunities
- Define the team´s technical roadmap aligned with company objectives
- Drive adoption of state-of-the-art ML techniques and tools
- Contribute to infrastructure decisions for scaling our ML platform

- Operational Excellence
- Implement robust CI/CD pipelines for ML models in Kubernetes environments
- Monitor model performance using MLflow tracking and implement drift detection
- Manage Flink jobs for real-time feature engineering and anomaly detection
- Document processes, architectures, and decision rationale

Requirements
Qualifications / Experience / Technical Skills
- Education & Experience
- Master´s or PhD in Computer Science, Machine Learning, Statistics, or related field
- 10+ years of hands-on experience in data science/machine learning
- 5+ years of experience leading technical teams
- Proven track record of deploying ML & LLM models to production at scale

- Technical Skills
- Deep expertise in Python and ML frameworks (PyTorch, TensorFlow)
- Extensive experience with commercial LLM APIs (Anthropic Claude, OpenAI GPT-4)
- Strong proficiency with MLflow for experiment tracking and model management
- Experience with distributed computing using Apache Spark
- Proficiency with Apache Flink for stream processing and real-time ML
- Knowledge of LLM fine-tuning techniques (LoRA, QLoRA, full fine-tuning)
- Expertise in anomaly detection algorithms and time series analysis

- Leadership Skills
- Demonstrated ability to lead and inspire technical teams
- Strong communication skills to translate complex technical concepts to stakeholders
- Experience with agile development methodologies
- Track record of successful cross-functional collaboration
- Ability to balance technical excellence with business pragmatism

Soft Skills / Personal Characteristics
- Experience with AIBrix, vllm or similar ML platform solutions
- Experience with AI code generation and anonymisation pipelines
- Knowledge of advanced prompting techniques and prompt engineering
- Experience building RAG (Retrieval Augmented Generation) systems
- Background in building ML platforms or infrastructure
- Familiarity with vector databases (Pinecone, Weaviate, Qdrant)
- Experience with model security and responsible AI practices
- Contributions to open-source ML projects