IND Applied AI Scientist
India GCC-Puppalaguda Village Full-time Mid-level AI Research
At a glance
Mid-level AI Research role at The Hartford. India GCC-Puppalaguda Village · full-time.
Pay not stated AI Research salary
RoleAI Research
SeniorityMid-level
LocationIndia GCC-Puppalaguda Village
EmploymentFull-time
PostedSep 18, 2026 · 4w ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Turn business needs into modeling questions, collect requirements, and plan experiments through production checks
- Build models and connect their scoring logic to live systems, including complex enterprise applications
- Set up model tests and measures, then support A/B trials, drift checks, and performance regression reviews
- Create NLP and GenAI solutions using embeddings, retrieval, chunking, prompts, structured outputs, and RAG
- Present model choices, tradeoffs, results, and risks in terms that technical teams and business partners can use
What you bring
- At least three years applying deep learning methods to real-world use cases
- Use Python with pandas, NumPy, scikit-learn, and SQL for modeling, data exploration, and feature work
- Have experience with evaluation and monitoring, including test sets, metric tracking, drift, and regression checks
- Bring hands-on work with unstructured data, production model integration, and cloud AI platforms
Who this fits
This mid-level role fits someone who can carry machine learning work from framing through deployment and monitoring, including NLP and generative AI. You'll need to explain model decisions and risks clearly to technical colleagues and nontechnical partners.
From the employer
IND Staff Engineer - GCC094
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 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 design, offline evaluation, and ongoing production validation and monitoring.
- Solid understanding and practical application of core machine learning methods, with 3+ years of experience applying deep learning architectures in real-world use cases.
- Experience designing and operationalizing model evaluation and monitoring approaches, including test set creation (gold and/or synthetic), metric definition and tracking (e.g., classification, forecasting, ranking/IR, and business KPIs), 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; 3+ years of exposure to basic container and cloud fundamentals supporting deployment 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 (e.g., Duck Creek, Ratabase).
- Experience with NLP and Generative AI capabilities, including embeddings, retrieval strategies (dense and hybrid), 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.