Machine Learning Engineer

Job OverviewJob ID: J38728Specialized Area: Machine learningJob Title: Machine Learning EngineerLocation: Austin, TXDuration: 12 MonthsDomain Exposure: Work Authorization: Client: To Be Discussed LaterEmployment Type: C2C (Consultant must be on our company payroll. C2C is not allowed)Responsibilities:Experience in design, development, and deployment of complex Client models and systems, ensuring they align with business goals and user needs.Able to architect and implement robust, scalable Client solutions, leveraging state-of-the-art techniques and frameworks such as PyTorch, TensorFlow, etc.Fine-tune and optimize large language models such as Mistral, LLaMA, etc., for specific use cases.Implement and experiment with cutting-edge NLP, NLU, and NLG techniques to enhance the capabilities and performance of our conversational AI products.Focused on monitoring and optimizing model performance, ensuring efficiency, accuracy, and fairness in production environments.Collaborate with software engineers to integrate machine learning models into production systems, ensuring scalability, reliability, and performance.Leverage tools and frameworks such as Docker, Kubernetes, ONNX, Kubeflow, MLflow, and other model serving platforms to optimize the deployment and management journey.Interested in staying abreast of the latest advancements in Client research, actively exploring emerging technologies and identifying opportunities for application within the company.Skilled in effectively communicating complex technical concepts to both technical and non-technical audiences, fostering seamless collaboration across teams (Client Engineers, Product Managers, Software Engineers).Requirements:A minimum of 2 years of professional experience as a Client Engineer.Bachelor's degree or higher in Computer Science, Machine Learning, AI, Mathematics, or related field.Excellent problem-solving abilities and a pragmatic approach to building scalable and robust machine learning systems.You have a strong foundation in machine learning and deep learning, including embedding methods, supervised and unsupervised learning, and deep learning architectures.Strong programming skills in Python and proficiency with machine learning libraries such as TensorFlow, PyTorch, or JAX.Experience with cloud platforms (e.g., AWS, GCP) and containerization technologies (e.g., Docker, Kubernetes).Candidates must have a strong foundation in statistics and an understanding of machine learning concepts, especially in NLP, NLU and NLG.Familiarity with the MLOps lifecycle, including deployment, monitoring, and orchestration of Client models in production settings.Experience with model deployment tools and platforms like TFServing, TensorRT, TorchServe, ONNX, Kubeflow, and MLflow.

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