- Company Name
- 55 Exec Search
- Job Title
- Senior Artificial Intelligence Engineer
- Job Description
-
Job Title: Senior Artificial Intelligence Engineer
Role Summary:
Design, build, and deploy advanced behavioural authentication models that analyse time‑series and multi‑modal sensor data to drive secure, adaptive digital identity decisions at scale.
Expectations:
- Deliver production‑ready models with measurable reductions in False Accept Rate (FAR) and False Reject Rate (FRR).
- Maintain end‑to‑end reproducible ML pipelines, experiment tracking, and model versioning.
- Collaborate with mobile and product engineering to integrate inference solutions into SDKs.
Key Responsibilities:
1. Develop, train, and evaluation deep learning models for behavioural authentication using time‑series and human interaction data.
2. Handle multimodal event‑driven sensor inputs (accelerometer, gyroscope, touch dynamics, device interaction).
3. Build and maintain data pipelines for irregular, asynchronous mobile sensor streams.
4. Design and experiment with transformer‑based, attention‑driven architectures for predictive performance.
5. Track experiments, models, and datasets with MLflow, ZenML, or equivalent workflows.
6. Optimize models for on‑device inference, balancing accuracy, latency, and hardware constraints.
7. Deploy edge‑inference models via CoreML, ONNX, and cloud services (SageMaker, AWS).
8. Contribute to large‑scale behavioural modelling infrastructure and shared training pipelines.
Required Skills:
- Strong experience with deep learning in PyTorch (beyond pre‑trained usage).
- Proficiency with time‑series and human‑behaviour data from sensors, wearables, or interactive logs.
- Expertise in building predictive models with transformer, attention, and custom head architectures.
- Mastery of reproducible ML workflows (MLflow, ZenML, Git, experiment management).
- Deployment experience on AWS (SageMaker, Lambda) and edge platforms (CoreML, ONNX).
- Advanced Python skills, including modern tooling (uv, poetry, pipenv).
- Practical mindset: ability to move models from research prototypes to robust production systems.
Required Education & Certifications:
- Bachelor’s degree in Computer Science, Machine Learning, Mathematics, or related field (PhD preferred but not mandatory).
- Relevant certifications in AWS (Solution Architect, Machine Learning) or ML frameworks are a plus.