AI/ML Engineer

As a high-impact AI/ML Engineer, you will lead the design, development, and deployment of machine learning and AI solutions across vision, audio, and language modalities. You will be an integral part of a fast-paced, outcome-oriented AI & Analytics team, collaborating with data scientists, engineers, and product leaders to translate business use cases into real-time, scalable AI systems. Your responsibilities in this role will include architecting, developing, and deploying ML models for multimodal problems encompassing vision, audio, and NLP tasks. You will be responsible for the complete ML lifecycle, from data ingestion to model development, experimentation, evaluation, deployment, and monitoring. Leveraging transfer learning and self-supervised approaches where appropriate, you will design and implement scalable training pipelines and inference APIs using frameworks like PyTorch or TensorFlow. Collaborating with MLOps, data engineering, and DevOps teams, you will operationalize models using technologies such as Docker, Kubernetes, or serverless infrastructure. Continuously monitoring model performance and implementing retraining workflows to ensure sustained accuracy over time will be a key aspect of your role. You will stay informed about cutting-edge AI research and incorporate innovations such as generative AI, video understanding, and audio embeddings into production systems. Writing clean, well-documented, and reusable code to support agile experimentation and long-term platform development is an essential part of this position. To qualify for this role, you should hold a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field, with a minimum of 5-8 years of experience in AI/ML Engineering, including at least 3 years in applied deep learning. In terms of technical skills, you should be proficient in Python, with knowledge of R or Java being a plus. Additionally, you should have expertise in ML/DL Frameworks like PyTorch, TensorFlow, and Scikit-learn, as well as experience in Computer Vision tasks such as image classification, object detection, OCR, segmentation, and tracking. Familiarity with Audio AI tasks like speech recognition, sound classification, and audio embedding models is also desirable. Strong capabilities in Data Engineering using tools like Pandas, NumPy, SQL, and preprocessing pipelines for structured and unstructured data are required. Knowledge of NLP/LLMs, Cloud & MLOps services, deployment & infrastructure technologies, and CI/CD & Version Control tools are also beneficial. Soft skills and competencies that will be valuable in this role include strong analytical and systems thinking, effective communication skills to convey models and results to non-technical stakeholders, the ability to work cross-functionally with various teams, and a demonstrated bias for action, rapid experimentation, and iterative delivery of impact.,