We are seeking a highly skilled Senior Advanced Software Engineer with deep expertise in computer vision, deep learning, and generative AI (GenAI). This role requires end-to-end ownership of machine learning pipelines, from research and prototyping to deployment and scaling in production environments. The ideal candidate will have profound knowledge of full-stack ML deployment and a proven track record of building robust, scalable systems.

• Strong background in computer vision, deep learning, and GenAI frameworks (e.g., Py Torch, Tensor Flow, Hugging Face).
• Expertise in end-to-end ML pipeline development (data engineering, model training, deployment, monitoring).
• Expertise in deploying ML models to Edge ( x86, arm64 architectures )
• Proficiency in cloud platforms (AWS, Azure, GCP) and containerization/orchestration (Docker, Kubernetes).
• Solid understanding of MLOps practices (CI/CD, model versioning, monitoring, retraining).
• Experience with full-stack ML deployment including APIs, microservices, and scalable inference systems.
• Strong programming skills in Python, C++/Java, and modern ML toolchains.
• Excellent problem-solving, communication, and leadership skills.

Preferred Qualifications

• Experience with distributed training frameworks (Horovod, Deep Speed).

• Knowledge of edge deployment for computer vision models.

• Familiarity with data pipelines (Spark, Kafka, Airflow).

• Contributions to open-source ML/AI projects.

• B.Tech/ M.Tech with 8+ years of experience

• Architect and develop end-to-end ML pipelines for computer vision, deep learning, and GenAI use cases.

• Design scalable solutions for data ingestion, preprocessing, training, evaluation, and deployment.

• Implement and maintain full-stack ML deployment frameworks across cloud and on-prem environments.

• Automate CI/CD workflows for ML models to ensure reproducibility and reliability.

• Monitor, troubleshoot, and optimize deployed models for performance, accuracy, and efficiency.

• Collaborate with cross-functional teams including data scientists, product managers, and DevOps engineers.

• Mentor junior engineers and contribute to technical knowledge sharing across the organization.

• Drive innovation by integrating emerging AI/ML technologies into production workflows.

• Optimize ML systems for large-scale data, distributed training, and high-throughput inference.

• Establish best practices for model versioning, monitoring, retraining, and lifecycle management