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Atlanta, GA, USA
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job responsibilities include:
Lead recovery and stabilization efforts for critical AI implementations by identifying root causes and executing remediation plans for customer-facing deployments.
Design, build, optimize, and support enterprise AI solutions leveraging Agentic AI frameworks, LLMs, RAG architectures, and machine learning technologies.
Debug and resolve complex failures in AI agents, workflows, prompts, integrations, and production-scale generative AI applications.
Re-architect non-deterministic AI workflows into scalable, deterministic solutions using validation frameworks, structured outputs, and workflow orchestration patterns.
Develop and support AI-enabled applications utilizing Python, TypeScript, cloud-native microservices, vector databases, and enterprise integration frameworks.
Integrate AI solutions with enterprise applications, APIs, databases, CRM platforms, and regulatory-compliant business systems.
Desired Qualifications:
Bachelor’s Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical field.
5–6 years of experience in AI/ML engineering, software engineering, or enterprise application development.
Strong expertise in:
Agentic AI Platforms & Infrastructure, Large Language Models (LLMs), Machine Learning (ML), Generative AI Solutions, RAG Architectures, AI Agent Frameworks
Hands-on experience with:
Python, TypeScript / JavaScript, GCP Cloud Platforms, Microservices Architecture, AI Orchestration Frameworks
Experience building, deploying, fine-tuning, and troubleshooting enterprise AI solutions.
Preferred Qualifications:
Experience with:
Database Architecture, Forward Deployed AI Engineering, Full Stack Software Engineering
Experience with LangChain, LlamaIndex, AutoGen, Gemini, or similar agent frameworks.
Knowledge of vector databases, enterprise observability, telemetry, and AI monitoring platforms.
Experience supporting regulated industries such as:
Banking, Financial Services, Healthcare, Retail
Any Graduate
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