Why This Role Stands Out
Advance your career by developing cutting-edge agentic AI solutions with a globally recognized leader in digital modernization, enjoying a hybrid work model that offers flexibility. If you possess strong Python development skills and a passion for Generative AI, LLMs, and AI Agents, you'll thrive in this role, contributing to impactful projects for Fortune 500 clients. Apply today to shape the future of AI applications!
Quick Overview
Job Description
Who are we?
For the past 20 years, we have powered many Digital Experiences for the Fortune 500. Since 1999, we have grown from a few people to more than 4000 team members across the globe that are engaged in various Digital Modernization. For a brief 1 minute video about us, you can check ;br />
We are looking for an Advanced Python Engineer / Agentic AI FDE with strong hands-on experience in Python development and a solid understanding of Generative AI, LLMs, AI Agents, and agentic application development.
The ideal candidate will work closely with client engineering and product teams to design, develop, integrate, and deploy production-grade agentic AI solutions. This role requires strong software engineering fundamentals, the ability to work with modern AI frameworks, and excellent client-facing problem-solving skills.
Key Responsibilities
- Design and develop scalable Agentic AI applications and AI-powered solutions using Python.
- Build, integrate, and optimize AI agents, multi-agent workflows, tools, and orchestration pipelines.
- Develop production-grade Python services, APIs, integrations, and backend components.
- Work with LLMs, prompt engineering, RAG, embeddings, vector databases, and tool/function calling.
- Implement agent workflows using frameworks such as LangChain, LangGraph, OpenAI Agent SDK, Google ADK, CrewAI, AutoGen, or similar frameworks.
- Integrate AI agents with enterprise systems, APIs, databases, SaaS platforms, and business applications.
- Develop and consume REST APIs, microservices, and event-driven integrations.
- Implement appropriate mechanisms for agent memory, context management, state management, and knowledge retrieval.
- Work with cloud-based AI platforms such as AWS Bedrock, Azure AI Foundry, or Google Cloud Vertex AI/Gemini.
- Implement observability, monitoring, logging, evaluation, and performance optimization for AI applications.
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