IND Lead Engineer, Data

Bot Jobs; India; On-site; INR 1,800,000 - 3,600,000; Full time

Job summary

Bot Jobs in India is seeking an IND Lead Engineer to drive AI and analytics initiatives in our insurance domain. You will own modeling work, design end-to-end ML pipelines, and collaborate with data scientists, software engineers, and product teams.

You should have hands-on Python, SQL, NLP, and ML framework experience along with building BI/data-lake solutions and deploying models in cloud environments. Strong communication and regulator-aware thinking are essential.

Qualifications

• Experience in statistical modeling, machine learning, and advanced analytics using Python, including pandas, NumPy, scikit-learn, and strong SQL for complex data exploration.
• 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 ML, deep learning, and NLP algorithms.

Responsibilities

• Lead the end-to-end ML lifecycle from data prep to deployment and monitoring.
• Design and implement BI and semantic layer solutions in data warehouses or data lakes.
• Develop and operationalize RAG pipelines and conversational AI capabilities.

Skills

Python

SQL

Statistical modeling

NLP

Pandas/NumPy

PyTorch

TensorFlow

Git

Unix

Tools

Vertex AI

Job description

IND Lead Engineer - GCC098

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.

About Us | Our Culture | What It's Like to Work Here

Every day, a day to do right. Showing up for people isn't just what we do. It's who we are – and have been for more than 200 years. We're devoted to finding innovative ways to serve our customers, communities and employees—continually asking ourselves what more we can do. Is our policy language as simple and inclusive as it can be? Can we better help businesses navigate our ever-changing world? What else can we do to destigmatize mental health in the workplace? Can we make our communities more equitable? That we can rise to the challenge of these questions is due in no small part to our company values that our employees have shaped and defined. And while how we contribute looks different for each of us, it's these values that drive all of us to do more and to do better every day.

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