United States - Wayne, PA, United States of America
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Onsite
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Full-time
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May 19
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Salary Undisclosed
Skills:PythonC#SQL.NET
Job Summary:
About the Role
We are looking for an AI/ML Engineer to lead the technical implementation of our generative AI roadmap. In this role, you won’t just be using AI to write code; you will be building the AI systems that power our enterprise. You will design and deploy intelligent agents, orchestrate complex workflows, and fine-tune models to solve real-world asset management challenges.
Our philosophy is centered on human augmentation. You will build tools that enhance expert judgment through sophisticated Agentic workflows, ensuring our AI implementations are scalable, secure, and execution-focused.
Job Description:
What You'll Do
Architect Agentic Workflows: Design and implement multi-agent systems and autonomous workflows using flow-based orchestration and chain-of-thought reasoning.
Database & Vector Management: Manage enterprise data and high-dimensional vector embeddings within PostgreSQL, ensuring efficient retrieval for RAG (Retrieval-Augmented Generation) pipelines.
LLM Integration & Optimization: Lead the integration of LLMs via Azure OpenAI, focusing on prompt engineering, model parameter tuning, and performance monitoring.
Fine-Tuning: Identify opportunities where fine-tuning specific models can improve domain-specific performance and lead the data preparation and execution process.
System Orchestration: Build and manage AI middleware that connects LLMs to PostgreSQL, enterprise data sources, and third-party APIs.
Technical Leadership: Create playbooks for AI development and mentor the team on LLM best practices, ensuring AI-generated outputs meet rigorous accuracy standards.
What We're Looking For
Required:
AI Specialization: 1+ years of experience building AI-powered applications, with a deep understanding of LLM capabilities, limitations, and prompt engineering.
Agentic Frameworks: Proven experience building AI Agents (e.g., using orchestration frameworks, LangChain, or flow-based visual programming tools).
Database Expertise: Strong proficiency in PostgreSQL (or other relational databases), including experience with relational schema design and ideally pgvector for managing vector embeddings.
Azure AI Ecosystem: Proficiency in the Azure environment, specifically Azure OpenAI Service and Azure App Services.
Model Fine-Tuning: Hands-on experience preparing datasets and executing the fine-tuning of open-source or proprietary models.
Core Engineering: Strong background in C#/.NET, ensuring AI features are built on a stable, enterprise-grade backend.
