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Company: The Hartford

Role: Senior Software Engineer/Applied AI Scientist

Location: Hyderabad, Telangāna, IN

Experience:

3+ years of experience applying deep learning architectures in real-world use cases
Experience across the end-to-end modeling lifecycle, including problem framing and requirements gathering, experiment design, offline evaluation, and ongoing production validation and monitoring
Experience designing and operationalizing model evaluation and monitoring approaches, including test set creation (gold and/or synthetic), metric definition and tracking, and supporting A/B testing, drift detection, and performance regression monitoring
Experience working with unstructured data, including document parsing and OCR fundamentals, text normalization, metadata and lineage awareness, and PII detection or redaction considerations
Experience using Git and Unix-based development environments, with experience building reproducible notebooks or pipelines and ensuring repeatable analytical workflows
Experience communicating modeling decisions, design tradeoffs, evaluation results, and risks to both technical and non-technical audiences, and translating analytical outcomes into measurable business impact
Experience working with cloud-based AI platforms such as Google Vertex AI, AWS SageMaker or Bedrock, or Azure AI Services, supporting experimentation, model training, and deployment
Experience deploying models and integrating scoring logic into production systems, including operation within complex enterprise or packaged application environments
Experience with NLP and Generative AI capabilities, including embeddings, retrieval strategies, chunking approaches, prompt engineering, structured outputs, and contributing to Retrieval-Augmented Generation (RAG) solutions and evaluations
Experience or exposure to advanced GenAI applications and extensions, such as agent or tool-use concepts, domain-specific knowledge graph integration, synthetic data generation, sentiment modeling, and GenAI use cases in filing or compliance contexts
Experience working within enterprise AI governance expectations, including aligning model development with compliance, privacy, documentation, and ethical standards

Key Skills:

Python
pandas
NumPy
scikit-learn
SQL
PyTorch
TensorFlow
Git
Unix-based development environments
Google Vertex AI
AWS SageMaker
Azure AI Services
NLP
Generative AI
Machine Learning

Role Focus:

Design & Deliver AI Solutions: Build statistical, ML, and generative/agentic AI solutions
Regulatory Intelligence & Filing Automation: Design and deploy GenAI capabilities to automate regulatory filing support
Knowledge Base Engineering for Strategic Domains: Engineer and maintain domain-specific knowledge bases
Domain & Compliance Integration: Develop deep understanding of The Hartford’s business structures, processes, and data sources
Stakeholder Collaboration: Partner with leaders and SMEs across Product, Operations, Claims, Underwriting, and Risk
End-to-End Solution Development: Own the AI lifecycle from problem framing through deployment
Unstructured Data & Retrieval Design: Prepare multi-format content
Prompt & Agent Design: Author robust system prompts, few-shot patterns, and structured outputs
Evaluation & Monitoring: Define metrics across use cases
Synthetic Data Generation & Augmentation: Develop and validate synthetic data pipelines
Customer Experience Optimization: Apply GenAI to elevate self-service, virtual assistants, and inspection automation
Architectural Collaboration & MLOps Integration: Partner with enterprise architects and platform teams
Innovation & Continuous Learning: Identify and pilot emerging methods

Nice to have:

RAG Expertise: Hands-on with vector databases and search
Document AI Tooling: PyMuPDF/pdfplumber, Apache Tika; OCR (Tesseract); layout-aware models (LayoutLM); table extraction (Camelot/Tabula)
Embedding Model Selection: Experience comparing OpenAI/Cohere/Voyage vs. open-source
Orchestration Frameworks: Familiarity with LangChain, LangGraph, or LlamaIndex
Cloud-Native ML: Hands-on with Vertex AI, SageMaker, or Azure ML
Responsible AI & Safety: Bias/fairness testing, hallucination mitigation, grounding checks, safety filters
Broader Modalities: Timeseries forecasting, recommenders, anomaly/fraud detection, speech/vision/multimodal
Fine tuning LLMs and Diffusion models using PEFT/LoRA, experience with distillation

Senior Software Engineer/Applied AI Scientist

Skills Required

Description

Experience:

Key Skills:

Role Focus:

Nice to have:

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