About this role
At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers’ evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection.
Job Description
In this role, you will design and build AI-driven Insight Studio for the Scholar product, transforming large-scale customer conversations into actionable, real-time insights. You will hands-on build and productionize AI systems, integrating LLMs, RAG pipelines, and AI agents into scalable, reliable applications that directly power business decisions.
Key Responsibilities
- Translate complex business problems into end-to-end AI solutions, leveraging machine learning and Generative AI techniques aligned with product and business goals.
- Design, build, and operate production-grade AI systems for conversational understanding, summarization, classification, and insight generation from large-scale customer interaction data.
- Architect and implement Retrieval-Augmented Generation (RAG) pipelines, integrating structured and unstructured data sources to deliver context-aware, grounded, and explainable AI outputs.
- Build and optimize vector search and embeddings-based systems, enabling semantic search, similarity matching, and efficient information retrieval at scale.
- Design and implement AI agent workflows, orchestrating multi-step reasoning, tool usage, and decision-making using frameworks such as LangChain, LangGraph, or similar.
- Apply prompt engineering and LLM orchestration techniques to improve output quality, consistency, latency, and cost efficiency in production environments.
- Integrate foundation models via platforms such as AWS Bedrock, including fine-tuning, evaluation, and deployment strategies tailored to business use cases.
- Build and maintain scalable data pipelines and distributed processing systems to support real-time and batch AI workloads.
- Own end-to-end lifecycle of AI/ML solutions, including data preparation, model development, evaluation (including LLM evaluation and guardrails), deployment, and continuous improvement.
- Implement MLOps and LLMOps practices, including CI/CD pipelines, versioning, monitoring, and automated evaluation for both traditional ML and AI systems.
- Monitor and optimize AI systems using observability tools (e.g., Datadog, CloudWatch), ensuring performance, reliability, and cost control in production.
- Analyze model outputs, communicate findings, and iterate on solutions to continuously improve business impact and user experience.
Essential Skills
Data Science & AI Expertise
- Strong proficiency in Python for AI/ML
- Proven experience building and scaling production-grade AI/ML systems
- Deep understanding of data pipelines, distributed systems, and scalable architectures
- Strong grounding in MLOps / LLMOps workflows and lifecycle management
- Practical experience building applications using: Large Language Models (LLMs) (prompting, fine-tuning, orchestration)
- Retrieval-Augmented Generation (RAG) architectures
- Embeddings and vector search systems
- AI agents and multi-step reasoning workflows
- Strong understanding of LLM evaluation, guardrails, and responsible AI practices
Frameworks & Tooling
- Hands-on experience with: LangChain, LangGraph, LlamaIndex or similar orchestration frameworks
- Experience designing modular, reusable AI pipelines and components
Data & Storage
- Experience with vector databases and modern data storage systems
- Familiarity with handling large-scale structured and unstructured datasets
Cloud & AI Platforms
- Experience with: Amazon SageMaker or similar ML platforms
- AWS Bedrock for foundation model access and deployment
- AWS services such as Lambda, S3, ElastiCache
- Experience deploying LLM-powered applications in cloud-native architectures
Observability & Reliability
- Experience with: Datadog, AWS CloudWatch
- Strong ability to monitor, debug, and optimize AI systems in production
Additional Platforms
- Experience with Microsoft Fabric or similar analytics platforms
Primary Skills
Artificial Intelligence Markup Language, Business Case Analyses, Data Analytics, Predictive Modeling, Waterfall Model
Shift Time
Shift B (India)
Recruiter Info
Shriya Kumari
About Allstate
Joining our team isn’t just a job — it’s an opportunity. One that takes your skills and pushes them to the next level. One that encourages you to challenge the status quo. One where you can shape the future of protection while supporting causes that mean the most to you. Joining our team means being part of something bigger – a winning team making a meaningful impact.
The Allstate Corporation is one of the largest publicly held insurance providers in the United States. Ranked No. 84 in the 2023 Fortune 500 list of the largest United States corporations by total revenue, The Allstate Corporation owns and operates 18 companies in the United States, Canada, Northern Ireland, and India. Allstate India Private Limited, also known as Allstate India, is a subsidiary of The Allstate Corporation. The India talent center was set up in 2012 and operates under the corporation's Good Hands promise. As it innovates operations and technology, Allstate India has evolved beyond its technology functions to be the critical strategic business services arm of the corporation. With offices in Bengaluru and Pune, the company offers expertise to the parent organization’s business areas including technology and innovation, accounting and imaging services, policy administration, transformation solution design and support services, transformation of property liability service design, global operations and integration, and training and transition.
Learn more about Allstate India here .