Job Description
Overview:
TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client is a global professional services firm specializing in digital transformation, business process management (BPM), analytics, and AI-driven solutions. The company partners with organizations to improve efficiency, transform operations, and drive business outcomes through a combination of process expertise, technology, and data insight.
TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client is a global professional services firm specializing in digital transformation, business process management (BPM), analytics, and AI-driven solutions. The company partners with organizations to improve efficiency, transform operations, and drive business outcomes through a combination of process expertise, technology, and data insight.
Job Title: Senior AI Engineer
Location: New York City, NY
Duration: 6 Months
Job Type: Temporary Assignment
Work Type: Onsite
Job Description:
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.
- 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.
- 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.
- 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.
Numbers & Facts
| Location | New York, NY |
| Industry | Computer/IT Services |
| Salary | $55–$60 |
| Company Size | 100 to 499 employees |
| Year Founded | 2009 |
| Website | http://www.tekwissen.com/ |
About Company
WE THE TEKWISSEN PEOPLE
TekWissen offers you a broader portfolio of services, industry-leading solutions, and the meaningful innovations that give you greater flexibility and speed to respond to market dynamics, reduced costs and risk to improve enterprise performance, and increased productivity to enable growth.
To keep pace with global market demands, TekWissen keeps its finger on the pulse of change. Our organized approach to guiding a project from its inception to closure. Managing projects is becoming more and more important as we enter the digital era. To cope with the pace that this transition demands, a method is required to manage projects so they can yield quality work, while incorporating efficient use of time and resources.
Project involves identifying which quality standards are relevant to the project and determining how to satisfy them.
It is important to perform quality planning during the Planning Process and should be done alongside the other project planning processes because changes in the quality will likely require changes in the other planning processes, or the desired product quality may require a detailed risk analysis of an identified problem. It is important to remember that quality should be planned, designed, then built in, not added on after the fact.
Capabilities and accomplishments in one TekWissen business enhance the opportunity for success in the others. Put simply, TekWissen's unique combination of attributes promotes success.Skills
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