AI and Machine Learning Engineer

Key ResponsibilitiesDesign and Develop ML Models for high impact engineering solutions. Build, train, and optimize machine learning and deep learning models to solve complex problems using natural language processing, computer vision, and predictive analytics. Knowledge of python, Pytorch, agentic frameworks, Langchain, RAG, capability to understand and build deep learning models on cloud or on premises compute.Data Collection and Preprocessing: Python preprocessing for structured and unstructured data [numeric, images, videos and documents)Feature Engineering: Identify, extract, and transform relevant features from raw data to improve model performance and interpretability.Model Evaluation and monitoring: Assess model accuracy and robustness using statistical metrics and validation techniques.Deployment and Integration: Knowledge of Kubernetes, Flask, Ray Serve, Azure Devops, ONNX, or cloud-based solutions. .Research and Innovation: Stay abreast of the latest developments in AI/ML research and technologies. Experiment with new algorithms, tools, and frameworks to drive innovation and maintain a competitive edge.Documentation and Reporting: Create comprehensive documentation of model architecture, data sources, training processes, and evaluation metrics. Present findings and recommendations to both technical and non-technical audiences.Ethics and Compliance: Uphold ethical standards and ensure compliance with regulations governing data privacy, security, and responsible AI deployment.Required Experience And SkillsEducation: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field. 4+ years of professional experience in machine learning, artificial intelligence, or related fields. A PhD or relevant research experience would be a plus.Hands-on experience with neural networks, deep learning, and architectures such as CNNs, RNNs, Transformers and Generative AI.Exposure to MLOps practices: monitoring, scaling, and automating ML workflowsExperience with big data platforms: Databricks, Hadoop, Spark, Dataflow, etc.Familiarity with advanced topics such as reinforcement learning, generative models, or explainable AIDesired Experience And SkillsProficiency in programming languages such as Python (preferred), Java, Csharp, or C++Deep understanding of machine learning frameworks: PyTorch, scikit-learn, Keras, etc.Experience with data manipulation tools: NumPy, SQL, PandasSolid grasp of statistics, probability theory, and linear algebraFamiliarity with cloud and Data computing platforms: Azure, Azure DevOps, DataBricks GCPKnowledge of containerization and orchestration: Docker, KubernetesExperience in deploying machine learning models to productionUnderstanding of software engineering best practices: version control (Git), unit testing, CI/CD pipelines

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