Senior Software Engineer (Python

Role Overview

AI Solution Development

• Design, architect, and develop scalable Generative AI applications and agentic solutions.
• Build and orchestrate AI agents using frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, or Microsoft Copilot Studio.
• Develop backend services, APIs, microservices, and data pipelines for AI-driven products.
• Implement RAG, Agentic RAG, tool and function calling, planning and reflection loops, and human-in-the-loop workflows.

AI Engineering and Integration

• Optimize prompts, system instructions, and agent workflows for reliability and accuracy.
• Integrate LLMs with enterprise systems, third-party APIs, vector databases, and knowledge repositories.
• Monitor and improve model and agent performance using observability tools, metrics, and user feedback.
• Collaborate with Product, Engineering, Data, and Design teams.

Documentation and Innovation

• Stay current with emerging AI technologies, frameworks, and best practices.
• Document architectures, design decisions, and reusable solution patterns.
• Support knowledge sharing across teams.

Required Qualifications

• 5–7 years of professional software development experience.
• Hands-on experience building Generative AI and LLM applications.
• Strong Python proficiency and production application development experience.
• Hands-on experience with at least two agentic AI frameworks.
• Experience with cloud AI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI/ADK.
• Strong backend skills including REST/gRPC APIs, asynchronous programming, Docker, and FastAPI or Flask.
• Understanding of leading LLMs and open-source alternatives.
• Practical experience building RAG solutions with vector databases.
• Expertise in prompt engineering, LLM orchestration, structured outputs, guardrails, ReAct patterns, and evaluation.
• Strong problem-solving, system design, and architectural decision-making skills.
• Excellent communication and collaboration skills across global teams.

Technical Areas

• Agentic AI frameworks include LangChain, LangGraph, Google ADK, CrewAI, AutoGen, and Microsoft Copilot extensibility.
• Cloud AI platforms include Azure OpenAI, AWS Bedrock, and Google Vertex AI/ADK.
• Vector database examples include Pinecone, Weaviate, ChromaDB, and Qdrant.
• Supported backend technologies include REST/gRPC APIs, asynchronous programming, Docker, FastAPI, and Flask.