IND Staff Software Engineer

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.

Staff Applied AI Scientist

We are seeking a Staff Applied AI Scientist to design, deliver, and scale enterprise-grade AI systems across agentic AI, generative AI, and applied machine learning. This role owns the full AI lifecycle, from problem framing and experimentation through deployment, monitoring, and governance, while partnering closely with business, engineering, platform, and risk teams to deliver responsible, high-impact AI solutions.

Primary Job Responsibilities

• Design, build, and operate production-grade AI systems including agentic workflows, RAG pipelines, conversational AI, decision assistants, and predictive ML solutions.
• Own end-to-end development: problem framing, hypothesis-driven analysis, data preparation, modeling, evaluation, deployment, and continuous improvement in production.
• Architect scalable multi-agent systems with tool/function calling, state and memory management, orchestration, and human-in-the-loop controls.
• Define success metrics and evaluation frameworks for AI systems, balancing accuracy, cost, performance, and reusability; enable A/B testing, drift detection, and failure-mode monitoring.
• Embed Responsible AI across the lifecycle, applying fairness, bias mitigation, explainability, privacy, safety-by-design principles, and ensuring governance, auditability, and regulatory alignment.
• Align solutions with enterprise AI architecture, security standards, AI lifecycle phases, and approved governance patterns.
• Partner with business stakeholders and subject-matter experts to ensure AI solutions are explainable, actionable, and aligned to measurable business outcomes and ROI.
• Collaborate with ML, data engineering, platform, and IT teams to deliver enterprise-ready systems using CI/CD, MLOps, observability, guardrails, and rollback strategies.
• Build reusable frameworks, pipelines, and assets to accelerate delivery and ensure consistent quality.
• Translate complex model behavior and tradeoffs into clear narratives for technical and non-technical audiences.

Skills

• Proven hands-on experience designing and scaling agentic AI systems, including multi-agent orchestration, tool/function calling, structured outputs, and HITL workflows.
• Experience with cloud AI platforms such as Vertex AI, SageMaker/Bedrock, or Azure ML across GenAI and traditional ML workloads.
• Strong knowledge of agent and workflow frameworks (e.g., LangChain/LangGraph, ADK) and structured outputs (JSON schemas, function/tool calling).
• Deep grounding in NLP and GenAI fundamentals: embeddings, chunking and indexing strategies, RAG design, prompt engineering, and agent patterns.
• Expertise in AI/ML evaluation frameworks, A/B experimentation, and continuous monitoring for performance, drift, and risk.
• Hands-on experience with vector databases and search platforms (e.g., Vertex AI Search, OpenSearch, pgvector/Postgres) and tradeoff analysis across quality, latency, and cost.
• Experience building document ingestion pipelines (PDF parsing, OCR, layout-aware extraction, table extraction, normalization).
• Strong Python and SQL skills, with solid foundations in statistics, experimental design, and ML fundamentals.
• Practical experience deploying and operating AI systems with experiment tracking, model registries, CI/CD, monitoring, and rollback strategies.
• Demonstrated application of Responsible AI practices, including bias testing, hallucination mitigation, grounding checks, explainability, and model risk documentation.
• Ability to operate in ambiguous, fast-evolving environments, applying sound judgment, structured decision-making, and critical thinking.
• Excellent communication and influencing skills, translating complex analyses into clear business insights.

Education, Experience, Certifications and Licenses

• 6-9 years of applicable Applied AI, data science, or machine learning experience.
• Bachelor’s degree in Data Science, Computer Science, Applied Mathematics, Machine Learning, Engineering, or a related analytical field; advanced degree is strongly preferred.