Python GenAI Solution Architect

Role & responsibilities

Design, architect, and develop scalable Generative AI applications and agentic solutions for real-world business use cases.

Build and orchestrate AI agents using frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, Microsoft Copilot Studio, or similar technologies.

Develop and maintain backend services, APIs, microservices, and data pipelines that power AI-driven products.

Implement advanced AI patterns including RAG, Agentic RAG, tool/function calling, planning & reflection loops, and human-in-the-loop workflows.

Engineer and optimize prompts, system instructions, and agent workflows to improve reliability, accuracy, and user experience.

Integrate LLMs with enterprise systems, third-party APIs, vector databases, and knowledge repositories.

Monitor, evaluate, and continuously improve model and agent performance using observability tools, metrics, and user feedback.

Collaborate closely with Product, Engineering, Data, and Design teams to deliver impactful AI solutions.

Stay current with emerging AI technologies, frameworks, and best practices, contributing innovative ideas to the team.

Document architectures, design decisions, and reusable solution patterns while supporting knowledge sharing across teams.

Preferred candidate profile

• 10+ years of overall software engineering experience. 
• 3+ years of hands-on experience building applications using Generative AI and LLM technologies. 
• Strong proficiency in Python and experience developing production-ready applications. 
• Hands-on experience with at least two agentic AI frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, or Microsoft Copilot extensibility. 
• Strong backend development skills, including REST/gRPC APIs, asynchronous programming, Docker, and frameworks such as FastAPI or Flask. 
• Solid understanding of leading LLMs including OpenAI GPT models, Anthropic Claude, Google Gemini, and open-source alternatives. 
• Practical experience building RAG solutions using vector databases such as Pinecone, Weaviate, ChromaDB, or Qdrant. 
• Expertise in prompt engineering, LLM orchestration, structured outputs, guardrails, ReAct patterns, and evaluation techniques. 
• Strong problem-solving, system design, and architectural decision-making skills. 
• Excellent communication skills with the ability to collaborate effectively across global teams. 

Preferred Qualifications

• Experience with AI observability and evaluation tools such as LangSmith, Ragas, DeepEval, Arize, or Weights & Biases. 
• Familiarity with model adaptation and fine-tuning techniques (LoRA, PEFT, RLHF concepts). 
• Experience with cloud AI platforms including Azure OpenAI, AWS Bedrock, or Google Vertex AI. 
• Understanding of CI/CD, MLOps, and LLMOps practices. 
• Exposure to graph databases, knowledge graphs, and structured data integration. 
• Experience with event-driven architectures and messaging platforms such as Kafka or RabbitMQ.