Agentic AI Engineer
New
United StatesFull-TimeMiddle
Salary161,500 - 218,500 USD per year
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Job Details
- Experience
- 5+ years in AI and machine learning, 1+ year of hands-on experience developing RAG-based solutions and agentic AI applications or prototypes.
- Required Skills
- AWSPythonGCPJavaPyTorchAzureContent managementTensorflowRPrompt Engineering
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, Data Science, or a related field.
- 5+ years of experience in AI and machine learning, including designing, building, and deploying AI models and systems.
- At least 1+ year of hands-on experience developing RAG-based solutions and agentic AI applications or prototypes.
- Proven experience building end-to-end AI workflows, not limited to prompt engineering but including full agentic system design.
- Strong programming skills in Python (preferred), R, or Java.
- Solid understanding of ML frameworks such as TensorFlow or PyTorch.
- Hands-on experience with agentic frameworks such as MCP, LangGraph, or LlamaIndex, including state management and tool orchestration.
- Strong understanding of prompt engineering, context management, and agent tool definition.
- Experience with cloud platforms such as Azure (preferred), AWS, Google Cloud, or OCI, including AI services like Azure AI Foundry, Bedrock, or Vertex AI.
- Strong data handling skills, including preprocessing, feature engineering, and model evaluation on large datasets.
- Master’s degree or advanced certifications in AI or Machine Learning are a plus.
Responsibilities
- Architect and implement production-grade multi-agent AI systems using modern orchestration frameworks, ensuring scalability, reliability, and security of agentic workflows.
- Build and maintain full AI pipelines covering data ingestion, embeddings, model integration, deployment, and continuous evaluation for performance and reuse.
- Develop and deploy internal AI applications that improve productivity through intelligent automation and decision support.
- Establish observability practices including logging, metrics, monitoring, and alerting to analyze and optimize runtime behavior such as latency, accuracy, and cost.
- Integrate agentic AI solutions with enterprise systems while applying AIOps principles for consistent deployment and operational efficiency.
- Collaborate with cross-functional teams including data scientists, software engineers, and product stakeholders to align AI initiatives with business goals.
- Stay current with emerging AI technologies and proactively recommend enhancements to existing systems and architectures.
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