Senior Engineer

Design and develop enterprise Generative AI solutions using Amazon Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index platforms.

• Define AI solution architectures and implementation approaches aligned with business and technical objectives.

• Design and implement Retrieval-Augmented Generation (RAG), Graph RAG, and Agentic AI architectures for enterprise use cases.

• Lead development of intelligent AI agents, tool-calling workflows, and autonomous task execution frameworks.

• Design and implement multi-agent orchestration solutions using frameworks such as LangGraph and evaluate emerging frameworks such as AutoGen or CrewAI where appropriate.

• Design and optimize prompt strategies, retrieval mechanisms, context orchestration, and response generation frameworks.

• Design prompt engineering pipelines, vector database integration, semantic search solutions, and Retrieval-Augmented Generation architectures supporting enterprise AI applications.

• Design and implement workflows using LangChain or LangGraph to support scalable AI application development.

• Design scalable AI engineering architectures incorporating authentication, authorization, asynchronous processing, scheduling, multithreading, API governance, and enterprise deployment best practices.

• Lead fine-tuning and model customization initiatives to improve domain-specific AI performance.

• Define AI integration patterns and deployment approaches for enterprise application ecosystems.

• Design cloud-native AI integration patterns supporting enterprise APIs, databases, messaging platforms, and event-driven architectures.

• Establish evaluation frameworks for AI response quality, reliability, relevance, and consistency.

• Design Human-in-the-Loop (HITL) workflows and evaluation mechanisms to improve AI quality, governance, and business reliability.

• Review AI solution designs to ensure adherence to engineering standards, scalability, maintainability, and responsible AI practices.

• Troubleshoot complex AI workflow, retrieval, orchestration, and model behavior challenges through detailed root cause analysis.

• Mentor team members on GenAI frameworks, RAG architectures, agentic systems, and AI engineering best practices.

• Drive continuous improvement initiatives focused on AI solution quality, innovation, and operational effectiveness.

Behavioral Competencies

• Demonstrates strong ownership while driving AI engineering excellence.

• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.

• Promotes innovation and quality-focused engineering through proactive experimentation and continuous improvement.

• Applies strong analytical thinking to evaluate complex AI, retrieval, and orchestration challenges.

• Demonstrates adaptability while managing evolving AI technologies, frameworks, and business requirements.

• Communicates effectively regarding AI solution design, risks, dependencies, assumptions, and improvement opportunities.

• Maintains high attention to detail across AI architecture, prompt design, workflow implementation, testing, and deployment activities.