AI / ML - Lead

Responsibilities

100% hands-on Experience is a must in development and deployment of production-level AI models at enterprise scale. (Build Vs Buy Decision Maker)

Drive innovation in AI / ML applications across various business domains and modalities (vision, language, audio).

Knowledge of Best practices in AI / ML, MLOps, DevOps, and CI / CD for AI / ML projects.

Continuously identify and evaluate emerging tools and technologies in the AI / ML space for potential adoption.

Qualifications

Overall 5 to 7 years of experience, out of which in 4+ in AI, ML and Gen AI and related technologies.

Proven track record of leading and scaling AI / ML teams and initiatives.

Strong understanding and hands-on experience in AI, ML, Deep Learning, and Generative AI concepts and applications.

Expertise in ML frameworks such as PyTorch and / or TensorFlow.

Experience with ONNX runtime, model optimization and hyperparameter tuning.

Solid Experience of DevOps, SDLC, CI / CD, and MLOps practices - DevOps / MLOps Tech Stack : Docker, Kubernetes, Jenkins, Git, CI / CD, RabbitMQ, Kafka, Spark, Terraform, Ansible, Prometheus, Grafana, ELK stack.

Experience in production-level deployment of AI models at enterprise scale.

Proficiency in data preprocessing, feature engineering, and large-scale data handling.

Expertise in image and video processing, object detection, image segmentation, and related CV tasks.

Proficiency in text analysis, sentiment analysis, language modeling, and other NLP applications.

Experience with speech recognition, audio classification, and general signal processing techniques.

Experience with RAG, VectorDB, GraphDB and Knowledge Graphs.

Extensive experience with major cloud platforms (AWS, Azure, GCP) for AI / ML deployments. Proficiency in using and integrating cloud-based AI services and tools (e.g., AWS SageMaker, Azure ML, Google Cloud AI).