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AI engineer in agentic systems
Job in
New York, New York City, Richmond County, New York, USA
Listed on 2026-10-26
Listing for:
Apoddo
Full Time
position Listed on 2026-10-26
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, AI Reliability/ Performance Engineer
Job Description & How to Apply Below
Описание:
We are looking for an exceptional AI Engineer to contribute to the design and development of our advanced Agentic AI systems. In this role, you will work closely with a dedicated AI engineering team, leveraging cutting-edge LLMs and multi-agent frameworks to build intelligent, autonomous workflows that integrate seamlessly into our core platform.
Задачи:- Build, and deploy production-ready, multi-agent workflows using frameworks such as Lang Graph and CrewAI in Python.
- Fine-tune, evaluate, and deploy both frontier LLMs (e.g., OpenAI, Anthropic) and open-source models (e.g., Llama, Mistral) tailored to specific domain tasks and datasets.
- Seamlessly integrate agentic systems into existing software infrastructure, ensuring high reliability, low latency, and scalable performance.
- Stay at the forefront of AI research and tooling, continuously evaluating new models, frameworks, and techniques to improve our AI capabilities.
- Minimum 5+ years of hands-on software and AI/ML engineering experience, with deep, production-level expertise in Python.
- Proven track record of designing and implementing complex AI agents and multi-agent systems using tools like Lang Graph, CrewAI, or Auto Gen.
- Strong practical experience prompting, fine-tuning, and evaluating Large Language Models (both proprietary/frontier and open-source).
- Ability to consistently overlap with the core team from 4:00 PM to 7:00 PM Belgrade time (CET/CEST).
- Будет плюсом:
Hands-on experience deploying and managing AI models using AWS Bedrock and the broader AWS generative AI ecosystem;
Experience operating within stringent security, governance, and compliance standards (e.g., banking or financial sectors);
Background in building evaluation frameworks for LLM outputs (e.g., RAG metrics, hallucination detection).
- Build real-world, production-facing agentic systems that solve complex, high-value problems.
- Fully remote setup with targeted, predictable overlap hours.
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