Machine Learning Engineer (Contract)

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• You care deeply about producing high-quality work that delivers real value
• You’re comfortable navigating ambiguity and solving complex problems collaboratively
• You bring strong expertise in your craft, alongside a willingness to keep learning
• You communicate clearly and build trust quickly with clients and teammates
• You’re pragmatic, adaptable and outcome-focused
• You value low-ego collaboration and enjoy working as part of multidisciplinary teams, * You have hands-on experience deploying and operating artificial intelligence and machine learning architectures in production ecosystems
• You are proficient in constructing high-volume batch processing systems, real-time microservices, automated continuous retraining, and system observability
• You possess deep understanding across the entire machine learning lifecycle, architecting sustainable solutions without compromising key product outcomes
• You demonstrate strong technical foundations in Python and SQL alongside modern modeling toolkits like PyTorch, TensorFlow, and Scikit-learn
• You bring practical expertise with public cloud environments, preferably Google Cloud Platform technologies such as BigQuery, Vertex AI, and Dataflow
• You are skilled with deployment pipelines, version control, and containerization platforms like Docker and Kubernetes
• You bring familiarity with advanced generative AI, Large Language Models, and agent orchestration tools such as ADK, LangChain, or AutoGen
• You articulate technical trade-offs effectively, influencing both engineering peers and non-technical business partners
• You are an inquisitive self-starter who pays attention to detail and continuously explores modern machine learning tooling

Not essential, but nice to have:

• A degree (BSc, MSc, or PhD) in Computer Science, Data Science, Engineering, or an equivalent technical field
• At least three years of professional experience operating as a Machine Learning Engineer or Data Engineer
• Strong appreciation of commercial KPIs and experience aligning technical model performance with broader business objectives