Senior Agentic AI Engineer

Quick Facts

• Role: Senior Agentic AI Engineer (hands-on engineering with architecture and system design)

Description

You will design, build, and evolve an AI-powered conversational analytics platform that enables users to explore their data, generate insights and visualizations, and make data-driven decisions using LLMs and agentic workflows. The role focuses on developing a scalable, secure, extensible agentic AI framework and integrating it with other products via standards such as Model Context Protocol (MCP). You’ll own features end to end, including evaluation, reliability improvements, and production-ready delivery.

Responsibilities

• Design, build, and evolve agentic AI workflows for data exploration, analysis, and conversational insights
• Develop and improve an extensible agentic framework (orchestration, reusable tools/skills, conversation management)
• Build secure integrations with application services and governed data sources, including sandboxed code execution and AI-powered data visualizations
• Create configurable solutions for client-specific prompts, tools, skills, and data definitions without duplicating core architecture
• Develop MCP servers and clients to enable communication and data exchange between agents and applications
• Contribute to architectural decisions and help migrate/evolve the existing LangChain-based solution
• Implement LLM evaluation, testing, observability, and security controls to ensure accurate, reliable, and data-grounded outputs
• Deliver features end to end from technical design through production release and continuous improvement

Requirements

• 6+ years of experience in software engineering, data analytics, or related fields
• 2+ years of agentic AI engineering experience
• Strong Python development skills for building, testing, and maintaining production-grade applications
• Hands-on experience building agentic AI systems using Pydantic AI, LangGraph, LangChain, or OpenAI Agents SDK
• Strong understanding of agentic patterns: tool calling, multi-step orchestration, context management, guardrails, and human-in-the-loop workflows
• Experience with MCP and multi-agent or cross-application integrations
• Experience building AI agents for data analysis, including dataset exploration, code generation and execution, and result interpretation
• Strong system design and architecture skills for configurable, extensible solutions and evaluating trade-offs
• Understanding of AI security, evaluation, and observability: secure data access, LLM testing, tracing, and performance monitoring
• Strong ownership, communication, and cross-functional collaboration; practical use of AI coding assistants

Benefits

• Build and shape the architecture and technical direction of an evolving agentic AI platform
• Work on production AI solutions where accuracy, data security, and reliability are essential
• Use modern AI technologies and development tools such as Pydantic AI, MCP, and OpenAI-based agent tooling
• Collaborative environment that encourages experimentation, continuous improvement, and technical ownership