Legal Operations Specialist at KLA optimizing legal workflows through AI implementation. Collaborating with legal and IT teams, enhancing efficiencies across various legal domains.
About the role
- Principal AI Architect at LSEG overseeing AI/ML platform design and development. Shaping future AI capabilities and driving innovation in machine learning and generative AI technologies.
Responsibilities
- Define and evolve the architecture for AI/ML systems, ensuring scalability, governance, data structures and paradigms, data lineage, shared runtime, common AI workspaces and performance.
- Conduct technical reviews of both code and AI/ML models, ensuring quality, reproducibility and adherence to groups and industry best practices.
- Own and enhance the AI/ML tooling ecosystem including experiment tracking, data pipelines, data access, model deployment pipelines, CI/CD for ML, and observability.
- Contribute to shared libraries and frameworks that support model training, evaluation, and deployment.
- Stay ahead of developments in GenAI and ML frameworks, and connect with the AI community.
- Apply SOLID principles, design patterns, and dependency injection to AI systems, ensuring maintainability and robustness.
- Ensure responsible AI practices, including model explainability, fairness, and compliance with data governance standards.
- Partner with product and platform teams to deliver intelligent, scalable solutions that solve real-world problems.
- Guide AI engineers and data scientists, fostering a culture of technical excellence, experimentation, and continuous learning.
Requirements
- Strong experience with supervised, unsupervised, and generative models.
- Excellent understanding of model lifecycle, from experimentation to production.
- Proven hands-on experience with key AI technologies and concepts such as LLMs, Scikit, prompt engineering, LangChain, vector databases, graph databases, Lucence, Lakehouse and retrieval-augmented generation (RAG).
- Strong grasp of software engineering principles (SOLID, design patterns), dependency injection, and scalable system design.
- Hands on experience with ML tooling such as MLflow, Snowflake, Databricks, SageMaker or Azure Machine Learning.
- Hands on production experience deploying and monitoring AI/ML systems on cloud platforms such as AWS, GCP or Azure.
Benefits
- Healthcare
- Retirement planning
- Paid volunteering days
- Wellbeing initiatives
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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