Build, and operationalize scalable AI/ML solutions that power Curacel’s core insurance products—automating claims adjudication, detecting fraud, and enhancing customer engagement—while collaborating closely with cross-functional teams to translate business needs into high-impact technical deliverables. This role aims to accelerate Curacel’s growth in emerging markets by driving continuous improvement of model performance, ensuring robust production deployments, and upholding the highest standards of data privacy, security, and ethical AI.
How You'll Help Us Achieve It
You will;
Develop, optimize, and maintain AI/ML models and pipelines for Curacel’s insurance products
Collaborate with cross-functional teams to understand business needs and translate them into technical AI solutions.
Deploy machine learning models in production environments with robust monitoring and performance tuning.
Perform data analysis, feature engineering, and experimentation to improve model accuracy and reliability.
Participate in code reviews, documentation, and knowledge sharing to promote best practices across the AI engineering team.
Stay current with AI research and emerging technologies relevant to insurance and healthcare applications.
Contribute to the design and implementation of scalable cloud-based AI services on AWS and Google Cloud.
Assist in troubleshooting and resolving AI system issues and production incidents.
Work in an agile environment, delivering incremental improvements and iterative prototypes.
Engage in mentoring junior engineers and collaborating on team skill development.
Requirements
4+ years of professional experience in AI/ML engineering or software engineering with AI focus.
Strong programming skills in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Experience with data engineering tools and ETL pipelines.
Hands-on experience deploying machine learning models to production using cloud platforms
Solid understanding of machine learning algorithms, model evaluation, and feature engineering.
Experience with containerization (Docker) and orchestration (Kubernetes) is a plus.
Familiarity with natural language processing (NLP) and computer vision techniques is advantageous.
Knowledge of healthcare or insurance domain data and regulatory constraints is a plus.
Comfortable working in a fast-paced, remote-first, collaborative environment with distributed teams.
Demonstrated ability to learn quickly, adapt to changing priorities, and take ownership.
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