A Snapshot of Your Day:

Join a forward-thinking team dedicated to harnessing the transformative power of AI within the energy sector. As an MLOps Engineer you will ensure that machine learning models are seamlessly integrated into workflow. You will proactively work with an existing AI Development team to design and deliver sophisticated, scalable, and resilient Generative AI based solutions on a Cloud Based platform.

How You’ll Make an Impact:

• Generative AI Application Development: Collaborate with developers and stakeholders in Agile teams to integrate LLMs and classical AI techniques into end-user applications, focusing on user experience, and real-time performance.
• End-to-End Pipeline Development: Build and maintain production-ready end-to-end ML pipelines, including data ingestion, preprocessing, training, evaluation, deployment, and monitoring; automate workflows using MLOps best practices to ensure scalability and efficiency. You are expected to be proficient in building and deploying models using AWS SageMaker and/or Azure ML Studio and able to monitor model performance, resource utilization and take corrective actions.
• Collaboration: Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes.

What You Bring:

• Bachelor’s degree in computer science, Data Science, or a related field with a strong understanding of machine learning concepts and algorithms.
• 2+ years programming proficiency in Python with AI/ML libraries is a must.
• 2+ years of experience with cloud platforms like AWS and/or Azure and their respective machine learning services like but not limited to Amazon SageMaker and/or Azure ML Studio.
• 2+ years’ experience in FAST API development , relevant REST API development in C#, .NET will be considered.
• Familiarity with DevOps practices and tools (Docker, K8s, Git)
• Solid Understanding of software engineering principles and practices
• Working knowledge Vector Databases and generating Embeddings is an add-on
• Hands-on experience in machine learning frameworks like TensorFlow or PyTorch is good to have.
• Ability to provide support and guidance to cross functional teams in MLOps practices.