About the Role
We are hiring a hands on AI Practice Lead to design, build, and scale production grade Agentic AI systems for enterprise clients, with a focus on regulated industries including financial services. This is a player coach role. You will architect and code the core platform yourself, and you will shape the practice, its playbooks, and its people as it grows. You will work directly with client executives, technical stakeholders, and internal delivery teams to take AI initiatives from strategy through deployment.
What You Will Do
- Design and build end to end Agentic AI systems that integrate large language models with enterprise data, APIs, and workflows.
- Architect and implement multi agent workflows using frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent, choosing the framework to fit the client problem rather than defaulting to one stack.
- Build and maintain reusable harnesses for prompt testing, agent evaluation, regression, and performance monitoring across releases.
- Implement observability, tracing, guardrails, security controls, and automated evaluation pipelines that meet enterprise and regulatory standards.
- Lead client discovery, use case shaping, and technical due diligence, translating business problems into agentic architectures with clear ROI.
- Establish the practice's technical standards, reference architectures, delivery playbooks, and hiring bar as the team scales.
- Partner with sales and account teams on solutioning, RFP responses, and executive presentations.
What You Bring
- 10+ years of software or ML engineering experience, with at least two years hands on with LLM based systems in production.
- Demonstrated ability to build and ship agentic systems, not just prototype them. Public repositories, published architectures, or client case studies preferred.
- Strong working experience with at least two of LangGraph, AutoGen, CrewAI, Semantic Kernel, LangChain, or LlamaIndex.
- Deep integration experience across REST and GraphQL APIs, event driven systems, vector databases, and enterprise platforms (Salesforce, ServiceNow, SAP, or similar).
- Hands on experience building evaluation harnesses, including offline eval sets, online metrics, human in the loop feedback, and automated regression.
- Practical grasp of observability tooling (OpenTelemetry, Langfuse, Arize, Weights and Biases, or similar) and of guardrail and safety approaches (input and output filtering, tool use policies, PII redaction, prompt injection defence).
- Exposure to regulated industries, ideally financial services, with a working understanding of data handling, audit, and compliance expectations.
- Strong executive presence and the ability to lead technical conversations with CIOs, CTOs, and business sponsors.
Bonus Experience
- Prior founder, practice lead, or head of AI role at a services firm or product company.
- Contributions to open source agent frameworks or evaluation tools.
- Experience with retrieval architectures beyond basic RAG, including Graph RAG, hybrid retrieval, and knowledge graph work.
- Domain background in FP&A, risk, KYC and AML, fraud, or other financial services functions.
Pay: From $100.00 per hour
Benefits:
Application Question(s):
- Have you personally built and shipped Agentic AI systems using LangGraph, AutoGen, CrewAI, Semantic Kernel, or a similar framework?
- Have you built reusable harnesses for testing, evaluating, or monitoring AI agents (including offline eval sets, automated regression, or human in the loop feedback)?
Experience:
- AI models: 8 years (Required)
- LLM Based Systems: 7 years (Required)
Work Location: Remote
