GenAI ML / MLOps Engineering Lead (Remote or Hybrid)

S&P Global; Victoria; Remote; CAD 110,000 - 160,000; Full time

Job summary

A leading company in financial services is looking for a GenAI ML / MLOps Engineering Lead to guide their machine learning operations within the Data Science Center of Excellence. The ideal candidate will lead engineering efforts for production-grade generative AI solutions, working closely with a world-class AI ML team and contributing to the company's AI-driven transformation while ensuring best practices in MLOps.

Qualifications

• 8+ years of experience in machine learning, data analytics, or similar roles.
• 5+ years of experience with MLOps / LLMOps, machine learning engineering.
• Experience operationalizing large-scale data pipelines.

Responsibilities

• Lead ML engineering efforts to architect, build, and deploy production-grade GenAI services.
• Work on large-scale distributed systems, including infrastructure and data ingestion platforms.
• Develop automated pipelines for deploying and maintaining models.

Skills

MLOps

Machine Learning Engineering

Data Analytics

Python

Containerization

Distributed Systems

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Elasticsearch

SQL

NoSQL

Apache Airflow

Spark

Kafka

Databricks

MLflow

Kubernetes

Cloud Platforms

Job description

GenAI ML / MLOps Engineering Lead (Remote or Hybrid)

Join to apply for the GenAI ML / MLOps Engineering Lead (Remote or Hybrid) role at S&P Global

About The Role :

We are seeking a Lead / Associate Director of ML & MLOps Engineering - GenAI to join our ML team within the Data Science COE at S&P Global, focusing on building Generative AI solutions. You will lead the engineering activities for building production-grade generative AI solutions, ensuring seamless deployment, monitoring, and management of models and data pipelines.

The Team :

You will work closely with a world-class AI ML team, including experts in AI ML modeling, ML & LLMOps engineers, data science, and data engineering. You will contribute to ML operations solutions and be a key part of S&P’s AI-driven transformation to deliver value internally and to customers.

Responsibilities and Impact :

• Lead ML engineering efforts to architect, build, and deploy production-grade GenAI services and solutions.
• Work on large-scale distributed systems, including infrastructure, data ingestion platforms, databases, microservices, and orchestration services.
• Develop MLOps / LLMOps platforms and automated pipelines for deploying, monitoring, and maintaining models, with governance, cost, and performance optimization.
• Collaborate with cross-functional teams to integrate machine learning models into production systems.
• Create and maintain documentation, best practices, and processes for MLOps / LLMOps.
• Work with technology teams on developing and implementing the Enterprise AI platform.

Qualifications :

Minimum Requirements :

• Bachelor's degree in Computer Science, Engineering, or related field.
• 8+ years of experience in machine learning, data analytics, or similar roles.
• 5+ years of experience with :
• Python (or Scala) coding at production level.
• MLOps / LLMOps, machine learning engineering, Big Data, etc.
• Tools like Elasticsearch, SQL, NoSQL, Apache Airflow, Spark, Kafka, Databricks, MLflow.
• Containerization, Kubernetes, cloud platforms, CI / CD, workflow orchestration.
• Distributed systems, AI / ML solutions architecture, Microservices.

Preferred Qualifications :

• 2-3 years of operationalizing large-scale data pipelines.
• Open-source contributions, research projects, Kaggle experience.
• Experience with RAG pipelines, prompt engineering, Generative AI use cases.
• Experience with SageMaker and / or Vertex AI.

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