AI Engineer

About the role

Agile Bridge is seeking an AI Engineer to transform promising models into secure, scalable, and reliable AI capabilities. You will work at the intersection of machine learning, deep learning, and software engineering, designing architectures, training models, and integrating them into production systems.

Key responsibilities

• Translate product and technical requirements into model objectives, architecture decisions and measurable acceptance criteria.
• Select, implement and train machine learning, deep learning, transformer or foundation‑model approaches suited to the problem.
• Prepare training and evaluation data, including preprocessing, feature engineering and dataset versioning.
• Run controlled experiments, tune models and evaluate accuracy, robustness, generalisation and computational efficiency.
• Package model‑backed capabilities as maintainable services, APIs, libraries or platform components.
• Build and contribute to CI/CD and MLOps pipelines for testing, registration, release and environment promotion.
• Deploy and monitor AI workloads, responding to drift, latency, failures, resource constraints and cost concerns.
• Embed security, responsible AI, traceability and clear technical documentation throughout the model lifecycle.

Required profile

• Bachelor's degree in Computer Science, AI, Machine Learning, Data Science, Software Engineering, Mathematics or a related quantitative field.
• 3‑5 years of experience building and operationalising machine learning or deep learning capabilities.
• Strong Python programming skills and solid software‑engineering practice.
• Hands‑on experience developing, evaluating and improving ML/DL models and integrating them into production environments.
• Ability to balance model quality with latency, scalability, reliability, security, maintainability and cost.

Required skills

• Python, SQL, C# or Java
• PyTorch, TensorFlow, scikit‑learn, Transformers
• Git, Docker, CI/CD, MLOps pipelines
• Model versioning, experiment tracking (e.g., MLflow)
• REST API design and consumption
• Data preprocessing, feature engineering, dataset versioning
• Cloud platforms (Azure, AWS, Google Cloud)
• Kubernetes, GPU workloads, distributed training
• Security, privacy, PII protection, responsible AI practices