Senior Agentic AI Engineer

United StatesRemoteFull-timesenior

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Design, develop, and deploy autonomous AI agents and multi-agent systems for real-world business applications. Architect agentic workflows incorporating planning, reasoning, memory, tool usage,

Requirements Summary

The ideal candidate combines extensive software engineering experience with strong expertise in production AI systems, agentic architectures, and scalable infrastructure.

Technical Tools

Machine Learning EngineerData

We are seeking an experienced Senior Agentic AI Engineer to lead the development of autonomous AI systems that transform business operations and enable smarter decision-making. In this role, you will design intelligent agents capable of reasoning, planning, executing tasks, and collaborating across complex workflows. You will shape the architecture of next-generation AI solutions, from advanced multi-agent systems to scalable Retrieval-Augmented Generation (RAG) pipelines. Working at the intersection of AI research and production engineering, you will turn emerging capabilities into reliable, secure, and practical applications. You will collaborate closely with product, engineering, and business teams to identify opportunities for intelligent automation and measurable impact. This is an opportunity to provide technical leadership, establish AI engineering best practices, and help define the future of agentic AI in a growing technology environment.

In this role, you will lead the design, implementation, and optimization of production-ready AI agents and intelligent automation platforms. Your responsibilities will include:

Design, develop, and deploy autonomous AI agents and multi-agent systems for real-world business applications.
Architect agentic workflows incorporating planning, reasoning, memory, tool usage, and autonomous execution.
Build and optimize Retrieval-Augmented Generation (RAG) systems, knowledge retrieval pipelines, embeddings, and vector database integrations.
Develop scalable agent orchestration frameworks using tools such as LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK.
Integrate AI agents with internal systems, APIs, databases, enterprise platforms, and third-party services.
Implement evaluation frameworks, monitoring, observability, and guardrails to ensure the reliability, security, and safety of AI systems.
Build collaborative multi-agent ecosystems that address complex operational and customer challenges.
Develop intelligent workflow automation solutions that improve productivity, operational efficiency, and business outcomes.
Collaborate with product, engineering, and business stakeholders to identify high-impact AI opportunities and translate them into practical solutions.
Drive technical architecture decisions, promote AI engineering best practices, and contribute to the organization's broader AI strategy.

Requirements

~2 min read

The ideal candidate combines extensive software engineering experience with strong expertise in production AI systems, agentic architectures, and scalable infrastructure. You should be comfortable taking ownership of complex technical challenges and translating advanced AI capabilities into reliable business applications. The key qualifications include:

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
At least 7 years of software engineering experience, including the development of scalable distributed systems.
At least 3 years of hands-on experience building and deploying AI/ML solutions in production environments.
Strong proficiency in Python and modern AI development frameworks.
Demonstrated experience developing AI agents, autonomous workflows, and agent orchestration systems.
Expertise in Large Language Models (LLMs), including platforms and models such as OpenAI, Anthropic, Gemini, Llama, Mistral, or equivalent.
Strong understanding of prompt engineering, tool calling, function execution, memory systems, and agent planning strategies.
Experience with vector databases, embeddings, semantic search, and Retrieval-Augmented Generation (RAG).
Solid knowledge of APIs, microservices, CI/CD pipelines, Docker, and MLOps practices.
Familiarity with agent orchestration frameworks such as LangGraph, CrewAI, or AutoGen.
Experience designing multi-agent collaboration systems and autonomous decision-making workflows is an advantage.
Knowledge of AI governance, security, compliance, and responsible AI practices is preferred.
Familiarity with knowledge graphs, reinforcement learning, or advanced reasoning architectures is a plus.
Experience working in a startup or high-growth technology environment is beneficial.
Strong problem-solving, architectural decision-making, and cross-functional collaboration skills.