IND Lead Engineer, Data

The Hartford India; Hyderabad; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

The Hartford India is seeking a Lead Engineer, Data to shape our advanced analytics and AI programs. You will design, develop, and deployed ML/AI solutions spanning NLP, data pipelines, and RAG-based chat capabilities for insurance contexts.

You will work with cloud AI platforms, big data, and analytics teams to translate business questions into production-ready models, while ensuring governance and regulatory considerations are respected.

Qualifications

• Proficiency in statistical modeling and ML with Python.
• Experience with end-to-end modeling lifecycle including requirements, experiments, offline evaluation and monitoring.
• Strong knowledge of NLP and Generative AI.
• Experience with data warehouses/data lakes and BI solutions.
• Familiarity with cloud AI platforms and deployment of AI/ analytics solutions.
• Ability to explain complex technical designs to technical and non-technical audiences.

Responsibilities

• Design and implement ML/AI models and analytics solutions.
• Develop and deploy conversational AI and RAG pipelines.
• Work with unstructured data, OCR, and document processing.
• Collaborate with data teams to define metrics and governance.
• Monitor experiments and evaluate model performance and drift.

Skills

Statistical modeling

Machine learning

Advanced analytics

NLP/GenAI

Communication of complex designs

Insurance domain knowledge

Tools

Python

SQL

pandas

NumPy

scikit-learn

PyTorch

TensorFlow

Vertex AI

SageMaker

Bedrock

Git

Unix

Jupyter Notebooks

Job description

IND - Lead Engineer, Data - GCC063

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.

• Experience in statistical modeling, machine learning, and advanced analytics using Python, including pandas, NumPy, scikit-learn, and strong SQL for complex data exploration, feature engineering, and knowledge preparation; familiarity with PyTorch and/or TensorFlow preferred.
• Experience across the end-to-end modeling and analytics lifecycle, including requirements gathering, experiment design, offline evaluation, and basic production monitoring and validation of AI and BI solutions.
• Solid understanding and practical application of core machine learning, deep learning, and natural language processing algorithms and architectures.
• Experience designing and implementing business intelligence and semantic layer solutions, including dimensional modeling, fact tables, metrics definition, and operating within data warehouse or data lake environments.
• Experience designing and operationalizing evaluation and monitoring strategies, including test set creation (gold and/or synthetic), defining and tracking metrics such as classification, forecasting, ranking/IR, RAG faithfulness and truthfulness, and customer or operational KPIs, with support for A/B testing and drift detection.
• Experience designing, developing, and deploying conversational AI solutions, including chatbots, virtual assistants, intelligent agents, and Retrieval-Augmented Generation (RAG) pipelines.
• Experience working with unstructured data, including document parsing and OCR fundamentals, robust text normalization, metadata and lineage awareness, and PII detection or redaction considerations for insurance documents.
• Experience working with cloud-based AI and analytics platforms such as Google Vertex AI, AWS SageMaker or Bedrock, or Azure AI Services, supporting experimentation, deployment, and conversational AI use cases.
• Experience using Git and Unix-based environments, building reproducible notebooks or pipelines, and applying basic container and cloud concepts to support reliable analytics and AI workflows.
• Experience communicating complex technical designs, trade-offs, evaluation results, and risks to both highly technical and non-technical business audiences, translating insights into clear business outcomes and strategy.
• Foundational knowledge of insurance products, processes (including underwriting, claims, and pricing), and regulatory environments, with demonstrated ability to apply analytics and AI solutions within a regulated industry context.
• Experience with advanced NLP and Generative AI capabilities, including embeddings, hybrid and dense retrieval strategies, advanced chunking, prompt engineering, structured outputs, and integrating knowledge graphs into RAG solutions for improved grounding.
• Experience or exposure to advanced GenAI applications in insurance, such as compliance-aware prompt engineering, document generation, objection response automation, synthetic data generation for low-frequency events, and customer sentiment modeling from surveys, call transcripts, or inspection notes.
• Experience working within enterprise AI governance frameworks, aligning conversational AI and analytics solutions with compliance, privacy, documentation, and ethical standards.