AI Solutions Engineer

• Strong experience in Python development
• Experience building LLM or agent-based applications using LangChain, LangGraph, or similar frameworks
• Hands-on experience with AWS Bedrock, including models, Agents, and tool/function calling
• Experience with AWS SageMaker and Bedrock Knowledge Bases
• Understanding of LLM limitations and practical challenges, including hallucinations, context management, and cost/latency trade-offs
• Experience designing end-to-end AI systems, including agent workflows and scalable AI services
• At least one real-world project involving AI agents deployed to production with measurable business impact or automation outcomes
• Experience creating custom tools/functions for AI agents
• Familiarity with evaluation, testing, and monitoring of LLM systems
• Strong communication and problem-solving skills
• English skills sufficient for technical communication and participation in project discussions

Nice to have:

• Hands-on experience with UiPath platform, including classic RPA and Agentic Automation
• Experience building and optimizing RAG pipelines (chunking, embeddings, retrieval tuning)
• Experience integrating AI agents with APIs, databases, and enterprise systems
• Familiarity with OpenSearch, vector databases, or semantic search solutions
• Experience with AWS Lambda, API Gateway, and S3
• Experience deploying and supporting AI solutions in production environments
• Familiarity with AI governance, observability, or GenAI security practices

• Design and develop LLM and agent-based applications using modern AI frameworks and AWS AI services
• Build scalable AI workflows and agent orchestration solutions using LangChain, LangGraph, or similar technologies
• Develop and maintain integrations between AI agents, APIs, databases, and external systems
• Work with AWS Bedrock services, including models, Agents, and tool/function calling capabilities
• Support implementation of RAG-based solutions, including embeddings, chunking, and retrieval optimization
• Create custom tools/functions for AI agents and automation workflows
• Contribute to architecture decisions for scalable and production-ready AI systems
• Support evaluation, testing, monitoring, and continuous improvement of LLM-based applications
• Collaborate with cross-functional teams to prototype and deliver AI-powered automation solutions