Associate Director - Machine Learning Engineer

This job is with S&P Global, an inclusive employer and a member of my Gwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

About the Role:

Grade Level (for internal use):
12  The   Role:  Machine Learning Engineer - Assoc Dir
The Team:
You will be work closely in a world class AI ML team comprised of experts in AI ML modeling, ML & LLMOps engineers, data science and data engineering teams. You will contribute to engineering and developing solutions for ML operations and be a critical part of leading S&P's AI-driven transformation to drive value internally and for our customers.
S&P is a leader in automation and AI/ML to transform risk management. This role is a unique opportunity for ML/MLOps engineers to grow into the next step in their career journey.

Responsibilities and Impact:
Lead the design, architecture, and implementation of production-grade GenAI services and solutions, ensuring scalability, reliability, and performance. This includes defining technical roadmaps and best practices for the team.

Drive the development and evolution of our large-scale, stateful and stateless distributed systems, encompassing infrastructure, data ingestion platforms, SQL and No

SQL databases, microservices, and orchestration services. Act as a technical authority and mentor to other engineers on these systems.

Champion and lead the development of our MLOps/LLMOps platform and automated pipelines, with a strong focus on deploying, monitoring, and maintaining models in production environments. Define and enforce model governance, cost optimization, and performance monitoring strategies.

Collaborate strategically with cross-functional teams (Product, Engineering, Research) to integrate machine learning models into production systems and define new AI-powered product features. Influence product direction based on technical feasibility and ML capabilities.

Establish and maintain comprehensive documentation and a robust knowledge base, including development best practices, MLOps/LLMOps processes and procedures. Ensure knowledge sharing and standardization across the team.

Provide technical leadership and guidance in the development and implementation of our Enterprise AI platform, proactively identifying opportunities for innovation and improvement. Contribute to the overall AI strategy for the organization.

Mentor and guide junior and mid-level ML engineers, fostering their technical growth and development.

Basic

Required Qualifications:

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

10+ years of progressive experience in data science, data analytics, machine learning engineering, or similar roles.

8+ years of relevant experience with:
Writing production-level, scalable code with Python.
MLOps/LLMOps, machine learning engineering, or a related role.
Search and analytics platforms (such as Elasticsearch, Solr, or Open Search), SQL and No

SQL database technologies, workflow orchestration tools (including but not limited to Apache Airflow, Prefect, or Dagster), distributed data processing frameworks (such as Apache Spark, Apache Flink, or Dask), streaming platforms (like Apache Kafka, Apache Pulsar, or Amazon Kinesis), cloud-based ML platforms (such as Databricks, AWS Sage Maker, or Azure ML), and ML lifecycle management tools (like MLflow, Kubeflow, or Weights & Biases).
Experience building with framework technologies (such as Lang Chain/Lang Graph/Lang Smith, Haystack, or similar AI application frameworks).
Containerization technologies (such as Docker, Podman, or containerd), container orchestration platforms (including but not limited to Kubernetes, Docker Swarm, or Open Shift), cloud platforms (such as AWS, Azure, or Google Cloud Platform), CI/CD platforms (such as Jenkins, Git Lab CI, or Azure Dev Ops), and workflow orchestration tools.
Distributed systems programming, AI/ML solutions architecture, and microservices architecture experience.

Additional

Preferred Qualifications:

3+ years of experience with operationalizing data-driven pipelines for large-scale batch and stream processing analytics solutions.

Experience with contributing to open-source initiatives or in research projects and/or participation in Kaggle competitions.

1+ year of experience working with RAG pipelines, prompt engineering, and/or Generative AI use cases.

Understanding of Agentic AI architecture, including key protocols like MCP, Google A2A.

LLM/Model API and inference framework experience.

Experience leading technical projects and mentoring junior engineers.

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