Senior Software Engineer/Applied AI Scientist

IND Software Engineer - GCC095

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.

Key Responsibilities

• Design & Deliver AI Solutions: Build statistical, ML, and generative/agentic AI solutions spanning RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems using a fit‑for‑purpose toolkit from traditional predictive modeling to agentic workflows.
• Regulatory Intelligence & Filing Automation: Design and deploy GenAI capabilities to automate regulatory filing support, including DOI objection response generation and ingestion of legacy filings into searchable knowledge bases. Partner with Legal and Compliance to ensure outputs meet evolving standards and enable direct API integrations with regulatory bodies.
• Knowledge Base Engineering for Strategic Domains: Engineer and maintain domain‑specific knowledge bases (regulatory intelligence, competitive insights, customer sentiment) to power generative applications across underwriting, pricing, and service.
• Domain & Compliance Integration: Develop deep understanding of The Hartford’s business structures, processes, and data sources. Embed domain taxonomies, regulatory constraints, access controls, and security into the solution design. Ensure adherence to responsible AI practices—fairness, bias mitigation, transparency, and observability with compliance‑by‑design.
• Stakeholder Collaboration: Partner with leaders and SMEs across Product, Operations, Claims, Underwriting, and Risk to align initiatives to business goals. Define success criteria that balance accuracy, reusability, cost, and performance. Translate model behavior into actionable strategies with clear ROI.
• End‑to‑End Solution Development: Own the AI lifecycle from problem framing through deployment: data prep, modeling, evaluation, CI/CD, orchestration, observability, safety filters/guardrails, and rollback plans. Collaborate closely with AI engineers, data engineers, platform, security, and IT for seamless integration.
• Unstructured Data & Retrieval Design: Prepare multi‑format content (PDF, Office, HTML, images, audio) with normalization, metadata/lineage management, and PII detection/redaction. Design retrieval strategies (chunking, embeddings, hybrid search) and tune for cost, latency, and domain fit; leverage rerankers where appropriate.
• Prompt & Agent Design: Author robust system prompts, few‑shot patterns, and structured outputs (e.g., JSON schemas). Define safe tool‑use policies and function/structured calling for reliable agent behavior.
• Evaluation & Monitoring: Define metrics across use cases for classification, information retrieval, RAG/chat, forecasting, plus customer/ops KPIs. Build gold/synthetic test sets, support A/B testing, and monitor drift. Provide economic, qualitative, and statistical analysis supporting thresholds and decisions.
• Synthetic Data Generation & Augmentation: Develop and validate synthetic data pipelines to alleviate sparsity and accelerate convergence, especially for low‑frequency perils and emerging segments, while preserving privacy and distributional fidelity.
• Customer Experience Optimization: Apply GenAI to elevate self‑service, virtual assistants, and inspection automation, driving personalization, speed, and operational efficiency.
• Architectural Collaboration & MLOps Integration: Partner with enterprise architects and platform teams to ensure scalable, secure deployments via unified systems. Standardize experiment tracking, registries, evaluation gates, and CI/CD patterns across clouds and services.
• Innovation & Continuous Learning: Identify and pilot emerging methods (OCR, rerankers, PEFT/LoRA, distillation). Build reusable accelerators (chunking templates, prompt registries, evaluation harnesses). Stay current on AI/ML, LLMOps, NLP, RAG, and responsible AI.

Required Skills & Experience

• Experience in statistical modeling and machine learning using Python, including extensive use of pandas, NumPy, scikit-learn, and strong SQL for data exploration, feature development, and knowledge preparation; familiarity with PyTorch and/or TensorFlow preferred.
• Experience across the end-to-end modeling lifecycle, including problem framing and requirements gathering, experiment desi…