Description
We're seeking an experienced AI/ML Engineer to drive digital transformation in our Energy software platform.
Qualifications:
- 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 processing building large-scale data stores for ML artifacts.
Scalable Services:
Design and implement low-latency scalable prediction 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 & operations fostering a collaborative cohesive work environment.
Architectural Leadership:
Take ownership of critical components of the platform providing architectural direction 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 engineering with a solid record of delivering production systems services.
Expertise in programming languages such as Python Java Go Scripting languages or SQL.
Demonstrated creative 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 distributed software architecture.
In-depth experience working with big data technologies including NoSQL Hadoop Spark Hive & data pipelines.
Strong expertise in data platforms encompassing design implementation scalable efficient storage retrieval processing systems.
Excellent communication collaboration skills fostering teamwork effective information exchange.
Familiarity with agile development methodologies including CI/CD test-driven development.
Working knowledge with cloud data processing training deployment operations such as Snowflake or Databricks.
Working knowledge of cloud networking principles their security implications for organizations holding sensitive data.
Preferred Qualifications:
Exposure to deploying ML-enabled projects solutions production environments.
Familiarity with Machine Learning Operations practices.
Exposure open-source Large Language Models on Hugging Face like Llama Mixtral.
Exposure ML libraries such as PyTorch TensorFlow XGBoost Pandas ScikitLearn.
Exposure statistical analysis.
Past collaboration with data scientists researchers.
Hands-on experience building on Kubernetes centric infrastructure CI/CD processes.
Hands-on experience automating vulnerability fixes working security teams at large enterprise companies.