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Application Analyst Principal
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United States
…aggregate the actual benefit received by projects to be reported to senior leaders (if applicable). Extrapolate long term lessons-learned and create recommendations regarding how to govern…
Added 1 min ago
E&S Strategic Staffing Manager 3
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United States
…individual selected for this role will also provide support to senior and executive leadership to enable execution and engineering…
Added 1 min ago
Engineering Business Operations Manager 2
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…individual selected for this role will also provide support to senior and executive leadership to enable execution and engineering…
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Principal Technical Support Engineer
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…Progress Federal Solutions Technical Support team. This is a senior, hands-on customer support role focused on helping U.S. Government customers operate mission-critical PDP environments.…
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Senior Paid Media Strategist
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Senior Paid Media Manager
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Senior Growth Marketing Strategist
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Senior Demand Generation Manager
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Senior Acquisition Marketing Manager
Clients Blackbox, Inc.
Remote, United States
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Added 4 mins ago
Performance Marketing Lead
Clients Blackbox, Inc.
Remote, United States
…Blackbox is in a major growth period, and we're looking for a Senior Paid Media Strategist / Paid Media Lead who can own client performance across media buying, strategy, messaging,…
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BuzzClan LLC

Sr. AI Engineer

New York, NY, United States

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Job details

Posting language: English
Posted: Nov 12, 2026

Original job description

Job Description

Senior AI Engineer (Agentic AI, Python & Risk Management)

Location: New York City, NY (Need local resource who can go for In-person)

Contract

Team: Firm Risk Management (FRM) Technology

Additional Points: Strong Python and hands-on LangGraph experience, including context/prompt engineering, memory, evaluation, and observability using Galileo or similar tools. Risk management knowledge is highly desirable. Strong Python, SQL, and data engineering skills, with practical understanding of AI agents, context/prompt engineering, memory, evaluation, and observability.

About the role

We are seeking a hands-on Senior AI Engineer to design, develop, and operationalize agentic AI applications for Firm Risk Management. Potential use cases include cross-risk variance analysis, credit risk lifecycle workflows, continuous news monitoring, and model risk lifecycle processes. This is a software engineering role requiring strong Python design and development skills and practical experience building AI agents with LangGraph. The successful candidate will own technical design and implementation, with particular focus on context quality, agent memory, evaluation, observability, and reliable operation in a governed enterprise environment.

Key responsibilities:

Design and build modular, maintainable Python applications, APIs, and services that integrate LLMs, enterprise data, and business tools.
Develop LangGraph-based agent workflows with explicit state, conditional routing, tool execution, checkpointing, and human-in-the-loop approval or escalation where appropriate.
Engineer the context supplied to agents: select and assemble relevant instructions, retrieved information, tool outputs, and conversation history within token and access-control constraints.
Design, version, and test prompts, including structured outputs, grounding instructions, and safe handling of incomplete or conflicting information.
Implement appropriate short- and long-term memory strategies, including persistence, retrieval, summarization, retention, and isolation between users and sessions.
Build evaluation frameworks with representative datasets, repeatable regression tests, and appropriate automated and human evaluation. Assess answer quality, grounding, tool-call correctness, task completion, and failure behavior.
Implement LLM and agent observability using Galileo or comparable platforms, including execution traces, quality signals, latency, token usage, cost, and failure analysis.
Apply guardrails for prompt injection, unsafe tool use, sensitive-data exposure, and unsupported outputs; incorporate access controls and auditable execution paths.
Collaborate with risk specialists, product owners, data engineers, and platform teams to translate requirements into tested applications and support deployment and troubleshooting.
Contribute technical design reviews, code reviews, documentation, and engineering guidance while remaining actively involved in coding.

Qualifications

Required skills and experience

Strong hands-on Python software engineering, including application design, asynchronous programming, APIs, automated testing, error handling, and maintainable code.
Demonstrated experience implementing AI agents with LangGraph, including stateful orchestration and integration with external tools or services - not only chatbots, framework tutorials, or conceptual designs.
Practical understanding of LLMs, embeddings, retrieval-augmented generation (RAG), vector search, and the tradeoffs involved in retrieval and context assembly.
Hands-on experience with context engineering, prompt engineering, and agent memory management.
Experience designing and implementing evaluation frameworks for LLM or agent applications, including test-set construction and interpretation of evaluation results.
Experience with Galileo or a comparable LLM/agent evaluation and observability platform, with the ability to trace failures and use findings to improve applications.
Experience delivering and supporting deployed applications, with sound understanding of security, reliability, version control, CI/CD, and production troubleshooting.
Ability to explain technical decisions, distinguish personal contributions from team outcomes, and collaborate effectively with business and engineering stakeholders.

Highly desirable

Knowledge of Model Risk Management, model development/validation lifecycles, model documentation, governance, or model monitoring.
Experience in broader risk management, particularly credit risk, market risk, or financial-services risk workflows.
Experience with Snowflake, Databricks, lakehouse architectures, enterprise data pipelines, or governed knowledge integration.
Familiarity with Model Context Protocol (MCP), cloud deployment, containers, and enterprise identity/access controls.
Prior experience using AI coding assistants such as GitHub Copilot, Claude Code, or Amp, with disciplined review, testing, and secure handling of code and data.

Additional Information All your information will be kept confidential according to EEO guidelines.

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More about this job

Skills

Python
Risk management
SQL
Data engineering
Troubleshooting
Documentation
Test automation
Assembly
CI/CD
Snowflake
Databricks
GitHub

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Jobs in the United States