Sr. AI Engineer - Inference & Agent Systems
About Our Client
Our client enables institutional investors to deeply understand portfolio risk, performance drivers, and market positioning through advanced analytics powered by proprietary datasets covering crowding, ownership, factor risk, and performance attribution.
Our client is building AI-powered systems that help investors isolate differentiated insights and make more informed decisions at scale.
About the Role
Our client is seeking a Senior AI Engineer (3–7 years experience) to build and optimize production-grade AI systems focused on LLM inference, agent orchestration, and evaluation frameworks. This role is highly execution-oriented and suited for an engineer who thrives in fast-moving environments and has successfully shipped AI applications used by real, paying customers.
You will work directly with technical leadership to design and implement multi-step agent pipelines, improve latency and reliability of inference systems, and ensure robust evaluation infrastructure capable of handling the non-deterministic nature of large language models.
This is an opportunity to operate as a strong individual contributor with meaningful ownership across the AI stack, helping shape the technical foundation of the company’s intelligence platform.
What You’ll Do
• Drive inference optimization to achieve Time to First Token (TTFT) below 400ms across multi-step agent workflows
• Design and implement Plan ? Execute ? Synthesize agent pipelines enabling sub-agents to run in parallel
• Own the evaluation framework end-to-end including ground truth dataset creation and regression detection
• Integrate state-of-the-art LLMs into production environments
• Build orchestration infrastructure leveraging frameworks such as Temporal
• Implement reliability patterns including retries, timeout handling, and graceful degradation
• Develop observability systems to trace token usage, tool calls, reasoning steps, and outputs
• Collaborate closely with product and leadership to deliver AI capabilities used daily by customers
• Design systems resilient to LLM non-determinism
Required Qualifications
Experience
• 3–7 years building and shipping production AI/ML applications
• Experience at an AI company with product-market fit (Series A+)
• Experience integrating LLMs into production systems
• Depth in inference optimization, agent architecture, or eval frameworks
• Strong individual contributor mindset
Technical Skills
• Strong Python proficiency for AI/ML development
• Experience with LLM orchestration frameworks
• Experience designing resilient distributed systems
• Strong understanding of observability for AI systems
Domain
• Experience in finance or fintech environments (portfolio analytics, trading systems, financial data platforms)
Education
• Degree in Computer Science or related technical field
Location
• Based in or willing to relocate to major U.S. tech hubs
• Remote flexibility may be considered depending on experience
Nice to Have
• Experience with Go (Golang)
• Experience working with financial datasets or investment analytics platforms
• Familiarity with vector databases and retrieval pipelines
• Experience working with Temporal or similar orchestration engines
• Experience optimizing latency in distributed AI systems
Traits That Tend Not to Be a Fit
• Frequent job changes (multiple roles under 2 years)
• Primarily academic experience without production deployments
• Experience limited to early-stage companies without scale exposure
• Heavy theoretical focus vs applied engineering execution
What Success Looks Like
• Agent pipelines operate reliably at production scale with strong latency performance
• Evaluation framework detects regressions automatically
• AI orchestration infrastructure supports rapid iteration
• LLM-powered workflows produce consistent outputs for financial analysis
• Observability enables rapid debugging of model outputs
• AI systems become a core competitive advantage for our client
