Senior AI Agent Engineer

Requirements

• 5+ years of relevant experience and a Master's or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, AI Engineering, or related fields.
• 3+ years of experience in AI, GenAI application development & deployment particularly with autonomous agent systems or related AI software engineering roles.
• Demonstrated ability to work independently in fast-paced, experimental environments.
• 3+ years of experience designing and building GenAI apps that allow users to experience AI use cases supporting features like agent orchestration, multi-step reasoning, prompt engineering, RAG integration, and model selection
• 3+ years of experience with LLMs and deep learning models, machine learning lifecycle management, data generation methods, model training & validation coupled with strong fundamentals and passion in software engineering and system architecture.
• Eligibility:
• Must be 18 years or older
• Legal authorization to work in the U.S. is required. We will not sponsor individuals at the Bachelor's level for employment visas, now or in the future, for this job opening.
• You must submit your application for employment on the careers page at www.gecareers.com to be considered
• Must be willing to work out of an office located in Niskayuna, NY or Cambridge, MA.
• GE Vernova offers a great work environment, professional development, challenging caree

Additional Information

Job Description Summary Tätigkeiten, die darauf abzielen, durch Anwendung wissenschaftlicher und technischer Methoden zu beweisen, dass ein Konzept gültig oder technisch machbar ist. Die Arbeit in dieser Familie hat in der Regel das Ziel, die Leistung und das Umsetzungspotenzial eines Konzepts durch Tests zu belegen oder zu analysieren. Beeinflusst Ansätze, Projekte und Programme im Funktionsbereich oder der betroffenen Unternehmensorganisation und die Arbeitsweise.Beeinflusst die Qualität, Effizienz und Effektivität des eigenen Teams.Hat wesentlichen Einfluss auf die Prioritäten.Geleitet von professionellen Praktiken und Richtlinien, die von der Rolle selbst geprägt sind.Die Rolle verfügt über eine moderate Autonomie und erfordert ein hohes Maß an operativem Urteilsvermögen. Job Description Roles and Responsibilities Design, implement, and optimize AI Agents using LLMs, reinforcement learning, planning algorithms, and decision-making frameworks. Develop scalable multi AI Agent architectures supporting long horizon reasoning, autonomy, planning, interaction, and complex task completion. Integrate AI Agents with APIs, backend services, databases, and enterprise applications. Prototype, deploy, and maintain AI-driven systems ensuring reliability and performance in production environments. Optimize agent behavior through continuous feedback, reinforcement learning, and user interaction. Collaborate closely with research, engineering, product, and deployment teams to iterate on agent capabilities and innovate continuously. Monitor AI Agent performance, conduct rigorous evaluations, implement safety guardrails, and ensure ethical AI practices. Document AI Agent architectures, design decisions, workflows, and maintain comprehensive technical documentation. Stay current with emerging AI technologies, contribute to platform and tooling improvements, and share knowledge within the team. Your mission is to push the boundaries of planning, reasoning, and agentic intelligence. You will design and implement large-scale AI/ML solutions integrating the latest state of the art research in our global products. Core Technical Skills: Proficiency in Python and/or languages like JavaScript, TypeScript, Node.js, or Java, Go, with strong coding and software engineering practices. Expertise with AI/ML libraries and frameworks such as LangChain, OpenAI APIs, PyTorch, TensorFlow, commercial or open source LLMs. Hands-on experience with LLMs, prompt engineering, and natural language processing (NLP). Knowledge of agent orchestration platforms and multi-agent systems (e.g., AutogenAI, LangGraph, MCP protocol). Familiarity with data management, vector databases, semantic retrieval, and real-time data pipelines. Experience deploying AI systems on cloud platforms (AWS, Google Cloud) with container orchestration (Docker, Kubernetes). Strong understanding of machine learning model training, fine-tuning, and evaluation techniques. Awareness of AI ethics, data privacy, and secure handling of sensitive information.

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