AI/ML Engineer

• Design, train, and optimize advanced ML models for CV, NLP, forecasting, recommendation, and anomaly detection.
• Build deep learning pipelines using PyTorch, TensorFlow, or JAX.
• Experiment with SOTA architectures such as Transformers, Vision Transformers, YOLO variants, RNN/CNN hybrids, and diffusion models.
• Lead rPPG and signal-processing experimentation for vital sign estimation (heart rate, HRV, BP estimation, etc.).

• Fine-tune and integrate LLMs (Llama, Mistral, OpenAI models).
• Build RAG pipelines, vector search systems, and semantic understanding workflows.
• Implement function-calling, tool integrations, and multi-agent AI systems.

• Build scalable ML pipelines, training workflows, and automated evaluation systems.
• Work closely with DevOps to deploy inference services using Docker, ONNX Runtime, TensorRT, or cloud-native APIs.
• Implement model monitoring, drift detection, and continuous improvement cycles.
• Optimize GPU/CPU performance for real-time inference.

• Work with large datasets across video, images, text, and tabular formats.
• Develop data preprocessing, feature engineering, and annotation workflows.
• Ensure data quality, labeling accuracy, and ethical dataset usage.

• Work closely with backend, mobile, and product teams to integrate ML into applications.
• Translate business requirements into ML-driven solutions.
• Mentor junior ML engineers and interns.

• 6+ years of experience in AI/ML engineering, deep learning, or applied research.
• Strong proficiency in Python, PyTorch, TensorFlow, and Scikit-learn.
• Expertise in Computer Vision (detection, segmentation, OCR, tracking, rPPG).
• Experience with LLMs, embeddings, RAG systems, and transformer architectures.
• Solid understanding of model optimization, quantization, and inference acceleration.
• Hands-on experience with AWS, GCP, or Azure for ML deployment.
• Strong math background in linear algebra, statistics, and optimization.
• Ability to design end-to-end ML systems from data ingestion to production.
• Strong debugging, problem-solving, and research mindset.

• Experience with ONNX, TFLite, TensorRT, or GPU inference pipelines.
• Knowledge of signal processing techniques for biomedical data (PPG/rPPG).
• Exposure to MLOps tools (DVC, MLflow, Kubeflow, Airflow).
• Familiarity with data annotation tools and active learning pipelines.
• Publications, Kaggle medals, or open-source contributions.
• Experience building SaaS AI products or enterprise-grade ML APIs.

Interested candidates may apply at [email protected]