As an ML Engineer within the ALSO Analytics Team, you will contribute directly to
data‑driven solutions that create tangible business impact across the organization. In this
role, you will work closely with internal business stakeholders, and domain experts totransform complex challenges into scalable, intelligent systems. Your technical expertise and
curiosity will help you spot opportunities in a rapidly evolving business environment, enabling
our teams to make better, faster, and more informed decisions.You will help shape the entire life-cycle of machine‑learning products—from early
experimentation and prototyping to scalable deployment and long-term maintenance. By
combining analytical rigor with hands‑on engineering skills, you will play a key role in
building the next generation of data products that support our colleagues in their daily work.

Key Responsibilities

Machine Learning Engineering

• Design and develop robust ML systems, models, and algorithms that address real
  business needs.
• Train, fine-tune, and optimize models to ensure high performance and reliability.
  Evaluate model behavior through experiments and benchmarking to guide technical
  decisions.

Data Workflows & Pipelines

• Prepare, clean, and analyze large and complex datasets to support ML development.
• Build, maintain, and improve automated ML pipelines—from data ingestion to model
  deployment.
• Ensure scalability and operational stability of end-to-end ML workflows.

Deployment & Operations

• Deploy ML models to production environments, ensuring they integrate seamlessly with
  business applications.
• Monitor, evaluate, and continuously improve existing models to maintain accuracy and
  relevance over time.

Communication

• Communicate insights, findings, and results in a clear and accessible way to both
  technical and non-technical audiences.
• Contribute to shared best practices, internal knowledge exchange, and the evolution of
  team workflows.

Required Skills & Qualifications

Technical Skills

• Contribute to shared best practices, internal knowledge exchange, and the evolution of
  team workflows.
• Strong programming skills, particularly in Python.
• Experience with major ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Expertise creating LLM solutions using base-models (Agents, Tool calling, Workflows)
• Solid foundation in statistics, mathematics, and data modeling techniques.
• Knowledge of neural network architectures and modern ML methodologies.
• Understanding of fundamental software engineering principles, version control, and
  CI/CD.
• Familiarity with cloud computing environments

Soft Skills

• Strong problem-solving abilities combined with creativity and initiative.
• Analytical and structured thinking, with attention to detail.
• Ability to communicate complex topics clearly and effectively.
• Comfortable working in cross-functional, collaborative teams.
• Curiosity and a mindset of continuous learning in a rapidly evolving field