Senior Agentic AI Engineer
J
JobgetherArtificial intelligence
Based remotely in United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 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.
- Required Skills
- PythonMLOps
Requirements
- Hold a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
- Have at least 7 years of software engineering experience, including scalable distributed systems development.
- Have at least 3 years of hands-on experience building and deploying AI/ML solutions in production.
- Demonstrate strong proficiency in Python and modern AI development frameworks.
- Have experience developing AI agents, autonomous workflows, and agent orchestration systems.
- Bring expertise in LLMs, including platforms or models such as OpenAI, Anthropic, Gemini, Llama, or Mistral.
- Understand prompt engineering, tool calling, function execution, memory systems, and agent planning strategies.
- Have experience with vector databases, embeddings, semantic search, and RAG.
- Have knowledge of APIs, microservices, CI/CD pipelines, Docker, and MLOps practices.
- Be familiar with agent orchestration frameworks such as LangGraph, CrewAI, or AutoGen.
- Bring strong problem-solving, architectural decision-making, and cross-functional collaboration skills.
Responsibilities
- Design, develop, and deploy autonomous AI agents and multi-agent systems for business applications.
- Architect agentic workflows using planning, reasoning, memory, tool usage, and autonomous execution.
- Build and optimize RAG systems, knowledge retrieval pipelines, embeddings, and vector database integrations.
- Develop scalable agent orchestration frameworks using 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 for AI system reliability, security, and safety.
- Build multi-agent ecosystems and intelligent workflow automation solutions for operational and customer challenges.
- Collaborate with product, engineering, and business stakeholders to identify AI opportunities and develop practical solutions.
- Drive technical architecture decisions, promote AI engineering best practices, and contribute to AI strategy.
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