Description
What you’ll bring to the table:
3–5 years of software engineering experience, including hands-on AI or machine learning application development.AWS Bedrock: Two end to end project implementationHands-on experience with AI Document IntelligenceStrong programming skills in Python and experience with another modern language such as TypeScript, JavaScript, Java, C#, or Go.Experience building backend applications, APIs, and cloud-native services using Microsoft Azure, AWS, or Google Cloud.Hands-on experience with LLM platforms such as Microsoft Foundry, AWS Bedrock, Google Vertex AI, OpenAI, Anthropic, or open-source models.Practical experience with RAG, embeddings, vector databases, prompt engineering, AI agents, and model evaluation.Solid understanding of software and AI architecture principles, including design patterns, distributed systems, vector search, MLOps/LLMOps, and emerging standards such as MCP.Experience with Docker, Git, automated testing, CI/CD, and modern software development practices.Demonstrated use of AI-assisted development tools such as GitHub Copilot, Cursor, or Claude Code to improve productivity, testing, code quality, and documentation.Strong troubleshooting, communication, and collaboration skills, with the ability to clearly explain technical approaches and trade-offs.
What you’ll do:
Build and maintain production-ready AI applications using LLMs, RAG, semantic search, and agentic workflows.Develop backend services, APIs, and integrations connecting AI solutions with enterprise systems, data sources, and cloud platforms.Build reusable agent components involving orchestration, tool use, memory, state management, and workflow automation.Implement document ingestion, chunking, embeddings, vector search, prompt workflows, and retrieval optimization.Develop evaluation, monitoring, and reliability processes to measure accuracy, hallucination, robustness, latency, safety, and cost.Contribute to CI/CD pipelines, containerized deployments, infrastructure-as-code, and MLOps/LLMOps practices.Apply secure and responsible AI practices, including access controls, guardrails, content filtering, prompt-injection protection, auditability, and human review.Collaborate with engineers, architects, data scientists, DevOps teams, and business stakeholders to deliver customer-facing AI solutions.Write clean, tested, maintainable code and clear technical documentation.
