More jobs:
AI Engineer
Job in
New York, New York County, New York, 10261, USA
Listed on 2026-02-13
Listing for:
The Phoenix Group
Full Time
position Listed on 2026-02-13
Job specializations:
-
Software Development
AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
- Collaborate with cross-functional teams and end users to create tailored AI agents and applications addressing complex business needs using platforms like Lang Chain, CrewAI, Auto Gen, or similar agent orchestration frameworks.
- Lead and contribute to greenfield AI projects, from conceptualization through to production deployment, shaping our broader AI strategy leveraging foundation models (GPT-4, Claude, Llama, Gemini) and vector databases (Pinecone, Weaviate, Chroma
DB). - Prototype using emerging AI tools and models, integrating solutions across platforms with a focus on usability, scalability, and impact through APIs, SDKs, and custom implementations.
- Develop best practices, frameworks, and reusable components using Python, and cloud platforms (AWS, Azure, GCP) to accelerate AI adoption and improve productivity.
- Build foundational AI components such as agent frameworks, reusable skills, and retrieval-augmented generation (RAG) systems using tools like Haystack, Llama Index, or Semantic Kernel that underpin innovative applications.
- Design, develop, and maintain shared AI infrastructure and agentic systems leveraging containerization (Docker, Kubernetes), workflow orchestration (Airflow, Prefect), and MLOps tools (MLflow, Weights & Biases) to enhance data integration and software development efficiency.
- Bachelors degree in a technical field such as Computer Science, Engineering, or similar discipline.
- Significant professional background in software engineering, particularly in creating and expanding artificial intelligence products across teams or entire organizations.
- Comprehensive knowledge of intelligent system technologies, encompassing autonomous agents, computational linguistics, statistical learning methods, and production implementation.
- Demonstrated success shepherding initiatives from initial design through live deployment within dynamic, team-oriented workplaces.
- Excellent reasoning and troubleshooting capabilities combined with inventive approaches to building intelligent applications.
- Hands‑on work with cutting‑edge artificial intelligence platforms, libraries, and model technologies.
- Understanding of organizational‑scale AI infrastructure and distributed system design.
- Background in data pipeline architecture and information retrieval methodologies.
- Previous contributions to shaping enterprise‑wide AI adoption roadmaps and engineering standards.
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