Associate

Role Overview
As an Associate Data/AI Engineer at PEG Alpha within the Bain Capability Network, you will design, build, and maintain scalable data and AI solutions addressing complex business challenges. You will apply expertise in data engineering, software engineering, and AI/ML to develop robust pipelines, intelligent applications, and reusable platform components.

Key Responsibilities

• Build and maintain data models, schemas, transformation layers, and reusable data components supporting analytics and AI-enabled applications.
• Develop and integrate advanced AI/LLM capabilities including Retrieval-Augmented Generation (RAG), semantic search, information extraction, summarization, copilots, and agentic workflows.
• Create robust ingestion, transformation, validation, and enrichment pipelines for enterprise data used in analytics, machine learning, and GenAI use cases.
• Work with relational, NoSQL, analytical, and vector databases to support application, analytics, and AI workloads.
• Develop APIs, services, and reusable components enabling secure consumption of data and model capabilities by applications and AI systems.
• Implement retrieval pipelines using embeddings, vector search, metadata filtering, reranking, and other techniques to improve LLM-powered application quality.
• Support evaluation and monitoring of AI/LLM solutions assessing retrieval/response quality, latency, reliability, and cost.
• Apply software engineering best practices including modular design, automated testing, version control, documentation, code review, and CI/CD.
• Optimize data and AI workloads for performance, scalability, reliability, and cost across development and production environments.
• Implement appropriate data quality, access control, security, privacy, and governance practices aligned with Bain's policies and responsible AI standards.
• Collaborate cross-functionally to translate business requirements into scalable technical solutions.
• Troubleshoot issues in data pipelines, applications, retrieval systems, and model integrations while improving observability and reliability.
• Evaluate emerging AI/LLM technologies contributing to technical decisions based on solution quality, scalability, security, maintainability, and cost.
• Use GenAI-assisted development tools responsibly (e.g., GitHub Copilot) to accelerate development while adhering to team engineering standards.