Lead ML Engineer

data engineering , Agile Development Methodologies, Continuous Integration, Microservices, system integration, Microsoft Azure, AWS, Apis, MLops, CodeWhisperer, cloud-native solution development, Hugging Face, vector databases, AWS Developer CLI, Agentic AI solutions, agentic AI orchestration, AI-assisted coding tools, fine-tuning LLMs, AI ML frameworks, vibe coding environments, LangChain, DevOps practices, prompt engineering, GitHub Copilot, OpenAI, real-time data processing

Exploratory Data Analysis, Sql, Tensorflow, Nosql, Azure ML, Pytorch, XGBoost, Python, Etl, data preprocessing, ML model evaluation, Scikit-learn, MLflow, Vertex AI, Kubeflow, feature engineering

Static timing analysis, Splunk, Unix, AI ML, Linux, Lsf, Python, Bitbucket, Jenkins, Git, DesignSync, EDA Tools, make, GenAI, Convolutional Networks, CI CD process, SW development and debug tools, Design Verification, reinforcement learning, Graph Neural Networks, Dft, Design revision control, Power-aware flows

object detection , Machine Learning, Artificial Intelligence, Tensorflow, Numpy, Git, Pandas, Pytorch, Opencv, Image Processing, Python, Computer Vision, Segmentation, CNNs, Deep Learning Architectures, Vision Transformers

containerisation with Docker, secrets management, pipeline automation with Azure DevOps, networking and load balancing, relational and non-relational databases, developing production-grade backend services using Python, asynchronous programming frameworks, cloud provisioning frameworks such as Terraform or CloudFormation, infrastructure-as-code, LLM API gateway technologies, container orchestration such as ECS Fargate, identity and access management technologies, observability and monitoring tools, multi-provider integration across major cloud AI platforms