Senior Machine Learning Engineer at Informa

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

21 Jan, 26

Salary

140000.0

Posted On

23 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Databricks, MLflow, Python, Spark, SQL, CI/CD, Shell Scripting, Big Data, AI, Model Deployment, Monitoring, Governance, Troubleshooting, Documentation, Communication, Team Collaboration

Industry

Information Services

Description

Company Description Curinos empowers financial institutions to make better, faster and more profitable decisions through industry-leading proprietary data, technologies and insights. With decades-long expertise in the financial services industry and a relentless focus on the future, Curinos technology and analytics ecosystem allows clients to anticipate customer needs and optimize their go-to market decisions in an increasingly competitive market. We operate in a hybrid/remote model, and this position is fully remote in the United States or hybrid in the Greater New York, Chicago, or Boston metropolitan areas. Job Description We are looking for a Senior Machine Learning Engineer to support our Databricks-native (i.e., Databricks Workflows, Delta Live Tables, Databricks Asset Bundles, Unity Catalog, etc.) machine learning and AI platform. In this role you will operationalize production-grade machine learning and AI models, manage deployments, and ensure robust monitoring and governance. In addition to solid technical skills, this role requires strong communication across functions, including data scientists, data engineers, and product managers; ability to drive clarity from requirements; and active participation and sense of ownership of end-to-end design, implementation, and testing of complex ML and AI workflows in a collaborative team environment. Responsibilities: Manage and support ML and AI model lifecycle using Databricks and MLflow: training, tracking, packaging, deployment, monitoring, agentic AI safety and generative AI guardrails. Automate and optimize model deployment pipelines for batch and real-time use cases. Collaborate with data scientists to productionize models, ensuring scalability, reproducibility, and reliability. Implement CI/CD pipelines for ML and AI workflows, including testing, version control, and rollback strategies. Monitor deployed models for performance drift, data quality, and SLA adherence. Support governance and compliance requirements: model registry usage, auditability, reproducibility). Troubleshoot and resolve issues related to model training, deployment, and integration with downstream systems. Provide operational support for Databricks Workflows, Jobs, and Delta Lake integration. Document processes, automation frameworks, and operational runbooks. Bring best practices to bear on the team’s products and educate the team members. Salary Range: $110,000 to $140,000 (plus bonus) Qualifications Desired Skills & Expertise: Strong hands-on experience with Databricks (Workflows, Delta Lake, MLflow). Proficiency with MLflow for experiment tracking, model registry, and deployments. Experience deploying models to Databricks Model Serving, REST APIs, or external endpoints. Familiarity with Python, Spark, and SQL for data and ML workflows. Solid understanding of CI/CD tools (e.g., Databricks Asset Bundles, Jenkins, etc.). Competency with shell scripting in a Linux environment nice to have Exposure to contemporary big data and AI stacks and technologies. Typical examples include things such as Yarn, AWS, Spark, Databricks, distributed computing, chatGPT, Claude, Copilot. but this is not an exclusive list. Our platform of choice is Databricks. Some experience with real-time processing nice to have Solid understanding of data structures and algorithms Some experience with production support Experience with 3rd party marketing clouds (such as Salesforce) nice to have Self–discipline and willingness to learn Solid verbal and written communication skills Team player and ability to work well with others in an intellectually challenging environment. Additional Information Why work at Curinos? Competitive benefits, including a range of Financial, Health and Lifestyle benefits to choose from Flexible working options, including home working, flexible hours and part time options, depending on the role requirements – please ask! Competitive annual leave, floating holidays, volunteering days and a day off for your birthday! Learning and development tools to assist with your career development Work with industry leading Subject Matter Experts and specialist products Regular social events and networking opportunities Collaborative, supportive culture, including an active DE&I program Employee Assistance Program which provides expert third-party advice on wellbeing, relationships, legal and financial matters, as well as access to counselling services Applying: We know that sometimes the 'perfect candidate' doesn't exist, and that people can be put off applying for a job if they don't meet all the requirements. If you're excited about working for us and have relevant skills or experience, please go ahead and apply. You could be just what we need! If you need any adjustments to support your application, such as information in alternative formats, special requirements to access our buildings or adjusted interview formats please contact us at [email protected] and we’ll do everything we can to help. Inclusivity at Curinos: We believe strongly in the value of diversity and creating supportive, inclusive environments where our colleagues can succeed. As such, Curinos is proud to be an Equal Opportunity Employer. We do not discriminate on the basis of race, color, ancestry, national origin, religion, or religious creed, mental or physical disability, medical condition, genetic information, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender identity, gender expression, age, marital status, military or veteran status, citizenship, or other protected characteristics.

Responsibilities

Manage and support the ML and AI model lifecycle using Databricks and MLflow, ensuring robust monitoring and governance. Collaborate with data scientists to productionize models and automate deployment pipelines for batch and real-time use cases.