Machine Learning Engineer

Machine Learning Engineer
About Us
We’re on a mission to redefine how insurance and lead-generation platforms operate through intelligent, data-driven automation. We’re looking for a Machine Learning Engineer with strong backend instincts to help productionize predictive models, design real-time decision engines, and build scalable data pipelines. If you’re passionate about ML deployment, feature engineering, and bridging the gap between data science and engineering, we want to talk.

Role Overview
As a Machine Learning Engineer, you'll take the lead on turning ML research into robust, production-grade systems. You'll work closely with engineering and infrastructure teams to build data pipelines, develop policy renewal prediction models, and implement real-time lead matching algorithms. This role is ideal for someone who thrives at the intersection of machine learning, backend architecture, and data operations.

Key Responsibilities

✅ ML Model Development & Deployment

• Build, train, and deploy machine learning models focused on policy renewal scoring and lead prioritization.

• Own the full ML lifecycle — from feature engineering and model selection to monitoring and retraining.

• Design and implement APIs and microservices to serve models in real-time production environments.

• Develop statistically rigorous A/B testing frameworks to validate model performance.

• Create monitoring tools and metrics dashboards to track model drift, accuracy, and latency.

✅ Data Engineering & Infrastructure

• Collaborate with backend engineers to design scalable data pipelines and ingestion systems.

• Use tools like Polars or Pandas to manipulate large datasets efficiently.

• Work with PostgreSQL and Redis to manage structured data, caching, and fast retrieval.

• Design workflows that allow for frequent retraining and continuous integration of model improvements.

✅ Backend Collaboration & Systems Thinking

• Implement and optimize data-intensive APIs (Fastify, Zod) for delivering ML outputs.

• Contribute to performance tuning and containerization (Docker) of ML services.

• Work within CI/CD pipelines to automate model testing and deployment.

• Use Terraform for managing cloud infrastructure and compute environments.

Technical Requirements

🔹 Machine Learning

• Strong experience with supervised learning techniques, time-series modeling, or classification algorithms.

• Proficiency in feature engineering, model evaluation (ROC/AUC, precision/recall), and hyperparameter tuning.

• Solid understanding of A/B testing, statistical significance, and online experimentation.

🔹 Data Engineering

• Proficient in data transformation using Polars, Pandas, or similar tools.

• Experience with scalable data pipelines and working with large datasets.

🔹 ML Ops & Backend Integration

• Familiarity with serving ML models using REST APIs or event-driven architectures.

• Experience deploying models using Docker and integrating into production environments.

• Knowledge of PostgreSQL and Redis in data-heavy applications.

🔹 Bonus Skills

• Experience with Node.js and TypeScript to collaborate with backend teams.

• Familiarity with infrastructure-as-code tools (e.g., Terraform).

• Exposure to lead scoring, recommendation engines, or insurance tech a plus.

Why Join Us?
Lead ML initiatives with real-world business impact
Own end-to-end model development and deployment
Collaborate cross-functionally with a fast-moving team
Remote-first with room to grow and innovate

If you're ready to build intelligent systems that scale — let's chat. Apply now and help shape the future of data-driven decision making.