Job Summary 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

- Solid 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)

📌 Data Scientist Senior Consultant II (Bengaluru)
🏢 Allstate India Private
📍 Bengaluru