Overview:You'll own the full lifecycle - from raw data ingestion to model deployment to measuring real-world business impact - with a current focus on building a sophisticated risk detection system using LLMs, Generative AI techniques, and classical ML within our SaaS platform. This is not a pure engineering role or a pure research role. You'll need both, and you'll need to move fluidly between them.
What You'll Do:Data Science & Applied ML- Research, prototype, and develop ML and LLM-based models to solve complex business problems, with a current focus on risk detection and prioritization
- Wrap models into production-ready APIs and integrate them into our core product
- Ensure model outputs are interpretable - translating predictions into actionable reason codes for end users
- Partner directly with operational teams to gather feedback, refine features, and improve model relevance over time
Data Engineering- Design, build, and maintain scalable pipelines to ingest data from disparate sources into our data warehouse/lake
- Implement robust data validation, quality checks, and transformation workflows across raw, curated, and serving layers
- Build and maintain curated datasets optimized for both analytics and model training use cases
MLOps & Production Ownership- Implement and maintain CI/CD pipelines for both data workflows and ML model deployment across environments
- Monitor pipeline latency, data drift, and model performance in production; design alerting and retraining triggers
- Own the business outcomes of your models - define success metrics, track ROI, and iterate based on real-world efficacy
- Manage infrastructure as code and containerized deployments to ensure reproducible, environment-consistent releases
What You'll Bring:- 5-8+ years spanning data engineering and data science/ML, with a demonstrated track record of shipping models to production
- Strong Python proficiency; experience with Spark/PySpark for large-scale data processing
- Advanced SQL for complex transformation, analysis, and data modeling
- Hands-on experience with cloud data platforms such as Databricks or Snowflake
- Experience with ETL/ELT frameworks - dbt, Lakeflow Declarative Pipelines, Databricks Autoloader, Informatica, or similar
- Familiarity with ML experiment tracking tools such as MLflow or Weights & Biases
- DevOps fluency: Git-based development, branching strategies, CI/CD, IaC (DABs/Terraform), and Docker
- Experience with orchestration tools such as Databricks Workflows or Apache Airflow
Strong Plus- Hands-on experience with LLMs and Generative AI techniques in a production context (prompt engineering, RAG architectures, fine-tuning, or evaluation frameworks)
- Experience building or operating ML platforms, feature stores, or model registries
- Prior work in risk, compliance, fraud detection, or other high-stakes ML domains
This job description is not designed to cover or contain a comprehensive listing of all activities, duties or responsibilities that are required of the employee. Duties, responsibilities and activities may change or new ones may be assigned at any time with or without notice.
Please note that visa sponsorship is not available for this position. We cannot support international remote work.
We do not accept unsolicited applications from third-party recruiters or agencies for this job posting. Any candidate submission without a prior agreement will be considered the property of our company, and we will not be responsible for any fees or obligations related to such submissions. We encourage interested candidates to apply directly through our official channels.
