Full Stack AI Engineer

Job Description

Mandatory Skills: Advanced GenAI & Agentic Framework Concepts, Cloud Application Integration & Deployment, AI Agents & Tool Calling, Azure AI Foundry, Agentic AI Systems, LangChain, LangGraph

Additional Skills: GitHub

Key Responsibilities:

• Develop and implement Generative AI solutions using platforms such as Amazon Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index services.
• Build Retrieval-Augmented Generation (RAG), Graph RAG, or Agentic AI solutions to support enterprise knowledge retrieval and intelligent automation use cases.
• Develop AI-powered applications leveraging Large Language Models (LLMs) aligned with business requirements.
• Develop secure AI services using Python and FastAPI, implement authentication, authorization, asynchronous APIs, request throttling, scheduling, multithreading, and scalable API design where applicable.
• Configure and implement AI agents, tool-calling mechanisms, and intelligent workflow automation solutions.
• Develop and maintain workflows using LangChain or LangGraph to orchestrate AI interactions and business processes.
• Implement prompt engineering, context management, and retrieval strategies to improve AI response quality and relevance.
• Support implementation of vector databases, semantic search, and prompt engineering techniques to improve retrieval quality and AI response accuracy where applicable.
• Support fine-tuning and model customization activities to enhance model performance for domain-specific use cases.
• Integrate AI applications with enterprise systems, databases, external APIs, cloud services, and Model Context Protocol (MCP)-based services where applicable to enable secure, scalable, and seamless business workflows.
• Perform testing, validation, and evaluation of AI-generated outputs to ensure quality, accuracy, and reliability.
• Monitor AI solution performance and support troubleshooting and optimization activities.
• Maintain technical documentation, prompt configurations, workflow definitions, and implementation artifacts.
• Collaborate with various teams and stakeholders to support successful AI solution delivery.
• Ensure AI solutions are reliable, scalable, maintainable, and aligned with business requirements.

Behavioral Competencies:

• Demonstrates ownership and accountability while delivering high-quality AI solutions.
• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.
• Maintains a quality-first mindset while developing reliable and effective AI-powered applications.
• Applies strong analytical and problem-solving skills to resolve AI, retrieval, and workflow-related challenges.
• Shows adaptability and willingness to learn evolving AI technologies, frameworks, and industry trends.
• Communicates clearly by sharing progress, risks, assumptions, and dependencies proactively.
• Maintains attention to detail during AI solution development, testing, validation, and deployment activities.
• Works effectively in dynamic environments with changing priorities and evolving business requirements.
• Takes initiative in improving AI solution quality, automation effectiveness, and operational efficiency.
• Demonstrates a customer-centric mindset by delivering reliable, high-quality solutions.Role & responsibilities

Preferred candidate profile

Perks and benefits