- Company Name
- Continuity
- Job Title
- Senior AI/ML Engineer
- Job Description
-
Job title: Senior AI/ML Engineer
Role Summary: Design, develop, and deploy production‑grade AI systems that enhance underwriting workflows for the insurance sector. Lead architecture of hybrid AI solutions combining machine learning, large language models, and orchestrated agents to create intelligent assistant tools and streamline risk assessment.
Expectations: Deliver end‑to‑end AI prototypes that transition into scalable, compliant production environments. Drive adoption of AI‑first practices and maintain high reliability, observability, and governance standards. Collaborate cross‑functionally to translate business goals into engineering actions and continuously iterate on data pipelines, model performance, and tooling.
Key Responsibilities:
- Architect hybrid AI systems that integrate ML models, LLMs, and agentic orchestration for use cases such as underwriting assistance, risk extraction, and document processing.
- Prototype high‑impact business use cases and structure AI agent teams to meet operational objectives.
- Evaluate and select open‑source or proprietary tools, ensuring scalability, compliance, and maintainability.
- Partner with product, data, and operations teams to move prototypes into production, establishing data collection, annotation, and synthetic data pipelines.
- Contribute to system architecture decisions, implementing modular agentic toolboxes, APIs, and observability (logging, monitoring, dashboards).
- Uphold AI governance, security, traceability, and ethical usage across all deployments.
- Champion AI‑first methodologies, CI/CD workflows, and continuous improvement of engineering practices.
- Monitor emerging AI technologies and propose adaptations aligned with business strategy.
Required Skills:
- 5+ years of experience building production AI/ML systems; deep knowledge of supervised/unsupervised ML, LLMs, RAG, and agentic architectures.
- Proficiency in Python, model deployment (Docker, Kubernetes), and cloud platforms (AWS/GCP/Azure).
- Experience with AI observability tools (Prometheus, Grafana, ELK) and CI/CD pipelines (GitHub Actions, GitLab CI).
- Strong understanding of data pipelines, annotation workflows, and synthetic data generation.
- Familiarity with AI governance, compliance, and ethical considerations.
- Excellent problem‑solving, communication, and collaboration skills.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, or related field (preferred).
- Certifications in cloud AI/ML services or relevant toolsets are advantageous.