Agentic AI Engineer

Sharp Decisions; New Jersey; Hybrid; USD 150,000 - 190,000; Full time

Benefits offered by this job

Hybrid on-site

W2 employment

Contract-to-hire

Job summary

Sharp Decisions is recruiting a senior, hands-on AI engineer to join the technology organization of a major global bank, anchored in Jersey City or Charlotte with hybrid on-site work. You will design, code, and deploy autonomous agents built on large language models, focusing on emergent behavior and systemic risk.

The role demands deep knowledge of AI agents, practical experience with LLMs, and strong Python (PySpark/SQL).

Qualifications

• Extensive experience building agents on top of LLMs.
• Strong coding in Python with data tooling (PySpark/SQL).
• Experience with Microsoft Azure in production environments.
• Understanding of complex systems, emergent behavior and associated risk.

Responsibilities

• Design and build LLM-powered agents and multi-agent systems from prototype to production.
• Write agent logic, tool use, orchestration, and data pipelines feeding agents.
• Model agent interactions within larger systems to identify emergent risk.
• Apply a complex-systems lens to interconnected processes, models, and data flows.
• Collaborate with engineering and business stakeholders to responsibly apply agentic AI across the bank.

Skills

LLMs

Python

PySpark

SQL

Azure

Education

PhD preferred

Tools

Databricks

Job description

Location: Jersey City, NJ or Charlotte, NC
Contract to hire
W2 only
Hybrid on site

About the Role

Sharp Decisions is recruiting for a senior, hands-on AI engineer to join the technology organization of a major global bank. This is a forward-looking role focused on agentic AI: designing, coding, and deploying autonomous agents built on large language models.

What sets this role apart is the problem space. The team is looking for someone who understands not just how to build an agent, but how agents behave once they interact with each other, with data, and with the institution around them. That means thinking about emergent risk, complex systems, and the interconnectivity of things.

What You’ll Do

• Design and build LLM-powered agents and multi-agent systems, from prototype through production.
• Write the code yourself: agent logic, tool use, orchestration, and the data pipelines that feed them.
• Model how agents interact within larger systems and identify where emergent behavior can create risk.
• Bring a complex-systems lens to how interconnected processes, models, and data flows affect one another.
• Work with engineering and business stakeholders to shape how agentic AI is applied responsibly across the bank.

What You Bring

• A deep understanding of AI agents: how they work, how they reason and act, and how they fail.
• Strong working knowledge of LLMs and practical experience building agents on top of them.
• Solid coding ability in Python, with PySpark and SQL experience for working with data at scale.
• Experience with Microsoft Azure.
• A genuine grasp of complex systems, emergent behavior, and systemic or emergent risk.

Backgrounds we’d love to see: Strong candidates often come out of computational social science or similar interdisciplinary programs, where modeling agents, networks, and emergent behavior is core to the work. A PhD in one of these fields is a meaningful plus.

Technology Environment

Azure, Python, PySpark, SQL, LLMs and agent frameworks. Databricks is part of the environment but not required.

Location

Jersey City, NJ or Charlotte, NC. The team is flexible on which office and will base the role wherever the right person is.

Sharp Decisions has connected technology talent with leading financial institutions for more than 36 years.
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