About the Role

We are looking for a highly experienced AI/ML Engineer with deep expertise in designing, training, deploying, and optimizing machine learning models for production environments.

You will lead complex AI initiatives across Computer Vision, NLP, predictive modeling, GenAI, and rPPG-based health analytics, supporting global clients and large-scale enterprise applications.
This role is ideal for someone who combines strong research understanding with hands-on engineering skills and can architect end-to-end ML systems.

Key Responsibilities:

Model Development & Research

• 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.).

GenAI & LLM Integration

• 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.

ML Engineering & Deployment

• 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.

Data Engineering & Analysis

• 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.

Cross-Functional Collaboration

• 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.

Required Skills

• 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.

Nice-to-Have Skills

• 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]