Senior Machine Learning Engineer, MLOps

Job Description:

Our Technology team drives the evolution of our Technology, Engineering, Data,

Product and User Experience functions. With a keen focus on delivering cutting-edge solutions, we shape the digital landscape for our customers, readers and users. From revolutionizing visuals to optimizing tools and harnessing the power of data, mobile, video and social platforms, our team is committed to providing a seamless and immersive experience across all touchpoints. Collaborating closely with our newsrooms and strategic partners, we spearhead the development of groundbreaking products and technologies.

We are seeking a highly experienced and technically adept Senior Machine Learning Engineer to join our team. In this pivotal role, you will own the entire lifecycle of MLOps from optimization to deployment, with a strong focus on LLMOps. This position demands a strong blend of hands-on technical expertise, strategic thinking, and the ability to foster innovation within a dynamic environment.

• Develop and manage LLM-based solutions, including semantic layer development, fine-tuning, model evaluation, deployment strategies, and the development of agent and multi-agent systems.

• Own end-to-end ML model development, optimization, and deployment.

• Ensure the scalability, efficiency, and reliability of ML pipelines.

• Design and implement robust model monitoring and retraining strategies.

• Optimize model inference and performance for production environments.

• Collaborate closely with data scientists, engineers, and product teams to integrate ML solutions.

• Improve experimentation frameworks, model versioning, and A/B testing strategies.

• Ensure best practices in MLOps, including automation, reproducibility, and CI/CD for ML.

• Contribute to architectural decisions and improve ML infrastructure.

You Have:

• A Bachelor's degree in Computer Science, Statistics, Mathematics, or a related quantitative field or equivalent work experience; a Master's degree or Ph.D. is a strong plus.

• 2-4 years of professional experience in machine learning with a strong portfolio of models deployed in a production environment.

• Strong experience with LLMOps, including fine-tuning frameworks, prompt engineering, and managing the lifecycle of large language models, as well as experience in the development of agent and multi-agent systems.

• Advanced hands-on experience with cloud-based data warehouse solutions; Snowflake experience strongly preferred.

• Proficiency in Python and SQL and deep, hands-on experience with ML frameworks like TensorFlow, PyTorch.

• A grasp of the broader data science toolkit, including libraries like Scikit-learn, Pandas, and NumPy.

• Experience building and maintaining MLOps pipelines using tools like MLflow, or Airflow.

• Experience with cloud platforms (AWS, GCP, or Azure) and large-scale data processing frameworks like Spark or Dask is preferred.