DataHat AI is hiring a Senior AI/ML Engineer who can turn advanced machine learning into working products. You will work across generative AI, virtual try on technology, computer vision, and traditional modelling. You should know how to connect research ideas with business value, explain model limitations in simple language, and guide teams through decisions. This role shapes how AI is applied in the product, from idea to deployment and measurable results.
Key Responsibilities
- Design, train, and refine models including diffusion models, GANs, virtual try on workflows such as segmentation, garment warping, and human parsing, and traditional predictive models.
- Build computer vision solutions using OpenCV or MediaPipe for segmentation, pose estimation, facial geometry, and apparel alignment.
- Develop complete ML workflows for training, deployment, and monitoring using Azure ML or similar platforms.
- Lead modelling work including feature engineering, experiment design, validation, retraining, and optimisation for accuracy or latency.
- Evaluate models using metrics such as FID, LPIPS, IoU, and accuracy, conduct bias analysis, and improve performance through iteration.
- Communicate findings, limitations, and trade offs to product and business teams in clear language.
- Mentor engineers and help build responsible MLOps practices.
Required Skills & Experience
- 2 to 5 years of hands on AI or ML engineering with strong knowledge in Python, PyTorch or TensorFlow, diffusion models, GANs, and ML modelling.
- Proven experience in computer vision systems including segmentation, pose estimation, or virtual try on solutions.
- Solid software engineering fundamentals with Git, Docker, Pandas or Scikit learn, and cloud ML platforms such as Azure ML or SageMaker.
- Ability to communicate technical concepts to non-technical audiences and influence decision making.
- MS or PhD in AI, ML, Computer Science, or equivalent experience with deployed generative or computer vision projects.
Preferred Qualifications
- Experience with MLflow or Kubeflow, A/B testing, experiment tracking, and big data tools like Spark.
- Knowledge of optimisation techniques such as quantisation or pruning, or domain experience in AR, beauty, or fashion tech.
