Gen AI - Senior Engineer

Job Description

Mandatory Skills:

Advanced GenAI & Agentic Framework Concepts, Cloud Application Integration & Deployment, Python, Azure OpenAI Service, Agentic AI Systems, AI Agents & Tool Calling, LangChain

Key Responsibilities:

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.
Encourages continuous improvement in AI engineering practices, framework adoption, and solution effectiveness.
Promotes secure, scalable, and responsible AI engineering practices while balancing innovation, governance, and business objectives.
Supports knowledge sharing and mentoring to strengthen team capabilities.
Balances innovation, scalability, reliability, and business priorities while driving delivery excellence.

Mandatory Competencies