Agentic AI Lead/ Architect (Python)

Envision, Inc

Alpharetta, GA, United States

29 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Microsoft Azure Continuous Integration Python (Programming Language) Natural Language Processing Azure Machine Learning Search Technologies Google Cloud Flask (Web Framework) Large Language Models

+9 more

Multi-Agent Systems Prompt Engineering IT Architecture Generative AI Fastapi Containerization Machine Learning Operations Virtual Agents Restful APIs

Job description

Design and architect Agentic AI and multi-agent systems using Python and modern LLM technologies.
Lead development of autonomous AI agents capable of planning, reasoning, tool usage, and task execution.
Design solutions using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar technologies.
Integrate LLMs, RAG pipelines, vector databases, APIs, and enterprise data sources.
Develop reusable AI-agent architectures, orchestration patterns, and tool-calling frameworks.
Define AI architecture, technical standards, security, governance, and best practices.
Build scalable AI services and APIs using Python, FastAPI, and cloud-native technologies.
Implement prompt engineering, context management, memory, evaluation, and guardrails.
Collaborate with product, data, engineering, and business teams to identify and deliver AI use cases.
Lead technical design reviews and mentor AI/ML engineers.
Monitor AI applications for performance, accuracy, reliability, cost, and latency.
Support deployment using AWS, Azure, or Google Cloud Platform, along with CI/CD and MLOps practices.

Requirements

Strong hands-on experience with Python.
Deep understanding of Generative AI, LLMs, NLP, and Agentic AI.
Experience with LangChain/LangGraph, AutoGen, CrewAI, or comparable agent frameworks.
Strong knowledge of RAG, embeddings, vector databases, and semantic search.
Experience with LLM APIs such as OpenAI, Azure OpenAI, Anthropic, or Google Gemini.
Experience developing REST APIs using FastAPI or Flask.
Knowledge of cloud platforms and AI/ML services.
Strong understanding of AI security, responsible AI, observability, and governance.
Excellent architecture, leadership, communication, and problem-solving skills.

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