Software / MLOps Engineer (Python, AWS)

We are looking for a ML / MLOps Engineer to contribute to production-grade AI systems within a fast-paced technology organization (insurance technology company). The team owns multiple customer-facing and internal AI applications, including real-time decisioning, operational automation, chatbots, and ML infrastructure. This is a production ML engineering role. Candidates who primarily focus on offline modeling or hand models off to other teams for deployment are not a good fit.

Responsibilities :

Design and build APIs and pub / sub event streams to support real-time machine learning inference and automated agentic processes.

Play a role in the development and maintenance of both online and offline feature stores for machine learning.

Gain familiarity with the property casualty insurance sector, including key policyholder and product attributes, to help enhance model effectiveness.

Implement industry-standard MLOps and LLMOps techniques to monitor ML models, feature sets, and agentic systems for performance degradation and data drift.

Support the ongoing development of our core MLOps platform, as well as the codebase and infrastructure for serverless AI applications.

Validate the performance of machine learning models through rigorous training and testing methodologies.

Collaborate with Data Science teams to engineer new features, construct transformation pipelines, integrate custom loss functions, and experiment with novel inference strategies such as chaining and shadow deployments.

Create and scale new agentic AI automations, guiding them from initial proof-of-concept through to full production deployment.

Construct evaluation frameworks designed to rigorously test AI applications, covering not only standard workflows but also the complex, real-world scenarios common to the car insurance domain.

Utilize the Python data ecosystem to execute machine learning projects and initiatives.

Take part in the team's weekly on-call rotation, addressing alerts promptly to maintain high service availability for both customers and internal stakeholders.

Requirements :

• Experience with Python (production-quality code)
• Experience with Python data science and machine learning libraries, including scikit-learn, pandas, numpy and related libraries.
• Hands-on experience deploying and operating ML models in production.
• Hands-on AWS experience (Lambda, Step Functions, DynamoDB, IAM, containers)
• Experience with Kafka or other event-driven systems
• Experience deploying ML models to production
• Git / CI / CD experience

Nice to Have