Data Scientist Senior Consultant II

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

Skills

Gen AI, Generative AI, LLM, Large Language Models, Python, RAG, Retrieval-Augmented Generation