Proficiency in Python and scientific computing libraries; working SQL skills for data exploration, feature engineering, and knowledge preparation.
Experience with hypothesis-driven experimentation, offline evaluation, and production validation within established CI/CD, MLOps, and governance frameworks.
Ability to define and track metrics for classification, information retrieval, RAG/chat systems, forecasting, and operational KPIs, including A/B testing and drift monitoring.
Working knowledge of embeddings, structured outputs, and foundational agent or tool-use patterns within approved guardrails and safety constraints.
Experience with document parsing and OCR fundamentals, layout-aware extraction considerations, normalization, metadata and lineage practices, and PII detection or redaction.
Hands-on experience with at least one cloud AI platform (Vertex AI, AWS SageMaker/Bedrock, or Azure AI Services).
Working knowledge of enterprise AI governance, compliance, privacy, fairness, transparency, documentation, and safety-by-design expectations.
Strong communication skills, translating analytical insights and evaluation outcomes into clear, decision‑relevant narratives.
Ability to operate with increasing autonomy in ambiguous problem spaces, applying sound judgment and escalating risks through defined governance processes.
Education, Experience, Certifications and Licenses
2 to 4 years of Applied AI Science or related experience, demonstrating increasing autonomy in delivering AI workstreams or complex solution components.
Bachelor's degree in Data Science, Computer Science, Applied Mathematics, Engineering, or a related analytical field. Advanced degree preferred.
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Additional Information
IND Senior Software Engineer - GCC123
We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.
Senior Applied AI Scientist
We are seeking a Senior Applied AI Scientist who will contribute to complex AI programs by owning defined workstreams or sub-components, delivering applied machine learning, generative AI, and emerging agentic AI solutions. This role applies established enterprise standards for Responsible AI, evaluation, and MLOps while partnering with cross‑functional stakeholders to translate AI capabilities into well‑governed, measurable business value.
Primary Job Responsibilities
Design, build, and deliver applied ML, generative, and agentic AI solutions for RAG, conversational assistants, document understanding, and classification as part of larger programs.
Drive assigned workstreams from problem framing and experimentation through deployment and monitoring, operating within approved enterprise AI platforms and delivery standards.
Apply Responsible AI practices across the lifecycle, including privacy, governance, fairness, transparency, explainability, grounding checks, hallucination mitigation, and safety-by-design; coordinate required reviews with Legal, Compliance, and Model Risk partners.
Contribute to knowledge base engineering, including document ingestion, metadata management, versioning, refresh processes, and auditability to improve retrieval quality and grounded AI behavior.
Author and iterate prompts, few-shot patterns, and structured outputs using approved templates; implement safe tool-use constraints, guardrails, HITL controls, and escalation paths aligned to use‑case requirements.
Define and execute evaluation plans for assigned components, selecting metrics and thresholds aligned to established frameworks; support A/B testing, drift detection, and failure-mode analysis.
Communicate progress, risks, tradeoffs, and evaluation results clearly, translating model behavior into actionable implications for stakeholders.
Partner with Product, Operations, Claims, Underwriting, Risk, and technical teams to align workstreams with business goals, data realities, and regulatory constraints.
Support production deployments and ongoing operations, adhering to monitoring, governance, observability, and rollback expectations.
Act as a role model for best practices, provide guidance to less experienced contributors, and participate in reviews that shape domain-level technical standards.
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