Job Description
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Company Overview
A technology organization building AI-powered solutions and modern software systems for enterprise environments. The organization focuses on combining artificial intelligence, software engineering, cloud technologies, and business transformation to help teams identify high-value opportunities and rapidly deploy production-ready solutions.
Responsibilities
- Engage directly with business stakeholders to understand workflows, challenges, and desired outcomes.
- Lead technical discovery sessions and translate ambiguous business problems into clear technical solution designs.
- Design, build, and deploy AI-powered applications and agent-based systems.
- Develop backend services, APIs, enterprise integrations, and automation workflows using Python.
- Build business-facing applications, dashboards, and user interfaces.
- Create and deploy AI agents and multi-agent systems using modern frameworks and enterprise tooling.
- Implement Model Context Protocol (MCP)-based integrations connecting AI applications with enterprise systems and data sources.
- Build data pipelines and workflows that support AI-driven applications.
- Define architecture decisions, technical standards, and implementation approaches.
- Deliver secure, scalable, and production-ready solutions on cloud platforms.
- Collaborate with infrastructure, product, data, and engineering teams throughout the development and deployment lifecycle.
Qualifications
- Advanced Python development experience building production applications and APIs.
- Experience designing and deploying production AI agents or agentic workflows.
- Hands-on experience with AWS Bedrock.
- Practical experience implementing or consuming Model Context Protocol (MCP).
- Full-stack development experience with modern frontend frameworks such as React, Vue, Angular, or similar technologies.
- Strong solution architecture and systems integration experience.
- Experience deploying production applications in cloud environments.
- Ability to work directly with stakeholders and lead technical discovery conversations.
- AWS AgentCore experience is a plus.
- Experience with AI agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or Semantic Kernel is a plus.
- Experience with Retrieval-Augmented Generation (RAG) architectures is a plus.
- Experience with vector databases such as Pinecone or similar technologies is a plus.
- Experience with LLM evaluation, observability, and guardrails is a plus.
- Docker and Kubernetes experience is a plus.
- Experience extending AI coding assistants through plugins, skills, or custom workflows is a plus.
Why This Role Is Interesting
- Opportunity to help build and shape a growing engineering capability.
- High ownership and direct impact on business solutions.
- Significant exposure to AI agent architectures and enterprise AI deployment.
- Opportunity to influence technical standards and reusable engineering patterns.
- Fast-moving environment where engineers own solutions from problem discovery through production deployment.
- Remote-first team with strong support for AI-driven innovation.
Disclaimer
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