Azure Senior and Principal MLOps Machine Learning Ops Engineer

We're seeking an experienced AI/ML Engineer to drive digital transformation in our Energy software platform.

Please consider applying if you have:
5+ years of commercial AI/ML experience
Azure expertise
Strong Python programming skills
MLOps and cloud infrastructure background

What You'll Do:
Design advanced ML solutions
Build scalable MLOps pipelines
Optimize AI model performance
Integrate cutting-edge AI capabilities

Technical Tools:
Azure Machine Learning
Kubernetes
Docker
CI/CD pipelines

Required Skills & Experience:

Hands-on experience with Azure ML, Azure Data Factory, Azure DevOps, and Kubernetes.
Strong expertise in MLOps frameworks, model versioning, and model monitoring.
Experience in banking, healthcare, or energy sectors handling regulated and sensitive data.
Proficiency in Python, Terraform, and containerization (Docker, Kubernetes).
Experience working in a complex enterprise environment with large datasets.
Knowledge of ML model development, training, and deployment workflows.
Ability to work cross-functionally with Data Scientists, Engineers, and Cloud teams

Responsibilities
Innovative System Design: Lead the design and engineering of software systems for the AI/ML Platform, contributing to the full ML development life cycle
Automation and Streamlining: Identify and implement opportunities to automate and streamline ML development processes, fostering efficiency and effectiveness
Workflow Automation: Develop comprehensive systems to automate and optimize laborious processes, integrating them seamlessly into our platform to streamline operations
ML Solution Deployment: Develop tools for building and deploying ML artifacts in production environments, facilitating a smooth transition from development to deployment
Big Data Management: Automate and orchestrate tasks related to managing big data transformation and processing, building large-scale data stores for ML artifacts
Scalable Services: Design and implement low-latency, scalable prediction, and inference services to support the diverse needs of our users
Cross-Functional Collaboration: Collaborate across diverse teams, including machine learning researchers, developers, product managers, software architects, and operations, fostering a collaborative and cohesive work environment
Architectural Leadership: Take ownership of critical components of the platform, providing architectural direction, and contributing to the overall success of the AI/ML Platform

Minimum Qualifications
BSc in Computer Science, or equivalent practical experience
3-8 years of experience in software development and engineering, with a solid record of delivering production systems and services
Expertise in programming languages such as Python, Java, Go, Scripting languages or SQL
Demonstrated created problem-solving skills with the ability to break down problems into manageable components
In-depth experience with Azure cloud technologies
Excellent track record in scalable system design and distributed software architecture
In-depth experience working with big data technologies, including NoSQL, Hadoop, Spark, Hive, and data pipelines
Strong expertise in data platforms, encompassing the design and implementation of scalable and efficient data storage, retrieval, and processing systems
Excellent communication and collaboration skills, fostering teamwork and effective information exchange
Familiarity with agile development methodologies, including CI/CD & test-driven development
Working knowledge with cloud data processing, training, deployment, or operations, such as Snowflake or Databricks
Working knowledge of cloud networking principles and their security implications for organizations holding sensitive data

Preferred Qualifications
Exposure to deploying ML-enabled projects and solutions to production environments
Familiarity with Machine Learning Operations practices
Exposure to open-source Large Language Models on Hugging Face like Llama & Mixtral
Exposure to ML libraries such as PyTorch, TensorFlow, XGBoost, Pandas, and ScikitLearn
Exposure to statistical analysis
Past collaboration with data scientists and researchers
Hands-on experience building on Kubernetes centric infrastructure and CI/CD processes
Hands-on experience automating vulnerability fixes and working with security teams at large enterprise companies