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.

Job Summary

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
- Solid 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

- LangChain, LangGraph, LlamaIndex 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

- 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

- Datadog, AWS CloudWatch

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Scientist Sr. Consultant II (India)
🏢 Elixir 360
📍 India