AI engineer for financial advisory

Elios, Inc.; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Elios, Inc. is seeking an AI Engineer to lead the design and build of production agents and LLM features for a specialized financial advisory firm.

You will own the work end to end, from architecture through production hardening to ensure reliable streams, deduplicated tool calls, and validated structured output. You will architect core runtime capabilities, develop a provider-agnostic LLM client, and shape evaluation gates to ship model and prompt changes with confidence, while mentoring

Qualifications

• Mid-level or senior experience building production LLM or agent systems, including tool/function calling, structured output, prompts, and streaming, and a working knowledge of how each one fails
• Strong systems engineering in Python (or a comparable backend language) with sound architectural judgment
• Rigorous testing discipline: dependency injection, boundary mocks, and tests that assert behavior rather than implementation
• Experience building evaluation pipelines or quality gates for model and prompt changes
• A mentoring instinct and a track record of raising the quality of the engineers around you
• Based in the UK or Germany and comfortable working fully remote
• Bonus: Hands-on Anthropic API experience: Claude tool usage, the Managed Agents API, MCP, or Skills; Familiarity with actor or agent frameworks and columnar analytics tools like DuckDB; Background in finance, accounting, audit, or another regulated domain

Responsibilities

• Lead the design and development of agents and LLM features end to end, from architecture through production hardening
• Architect core agent-runtime capabilities: session lifecycle, SSE streaming with reconnect and de-duplication, crash-safe tool dispatch, and structured and streaming model integration
• Build out the provider-agnostic LLM client (text, JSON-schema structured output, streaming tool use) and the prompt registry behind it, with templating and versioning
• Own the evaluation frameworks and quality gates the team relies on to ship model and prompt changes with confidence
• Take on the ambiguous, high-impact problems, like streaming reliability, tool-call correctness, and cost versus latency trade-offs
• Telemetry over LLM requests and background jobs, so token usage, latency, cost, and outcome quality are always visible
• Compose tools and actions across both self-hosted and cloud-sandbox execution models
• Work on the integration layer and its mock seams, plus columnar analytics where agents reason over data
• Mentor engineers on craft, conventions, and judgment. A big part of this role is setting the patterns others follow.

Skills

Python backend

Systems engineering

Testing discipline

Evaluation pipelines

Mentoring

Job description

Описание

We're hiring an AI Engineer to lead the design and build of production agents and LLM features for a specialized financial advisory firm. You'll own the work end to end, from architecture through the unglamorous production hardening that decides whether an agent actually holds up: streams that reconnect cleanly, tool calls that never fire twice, structured output that validates every time.

Задачи

• Lead the design and development of agents and LLM features end to end, from architecture through production hardening
• Architect core agent-runtime capabilities: session lifecycle, SSE streaming with reconnect and de-duplication, crash-safe tool dispatch, and structured and streaming model integration
• Build out the provider-agnostic LLM client (text, JSON-schema structured output, streaming tool use) and the prompt registry behind it, with templating and versioning
• Own the evaluation frameworks and quality gates the team relies on to ship model and prompt changes with confidence
• Take on the ambiguous, high-impact problems, like streaming reliability, tool-call correctness, and cost versus latency trade-offs
• Telemetry over LLM requests and background jobs, so token usage, latency, cost, and outcome quality are always visible
• Compose tools and actions across both self-hosted and cloud-sandbox execution models
• Work on the integration layer and its mock seams, plus columnar analytics where agents reason over data
• Mentor engineers on craft, conventions, and judgment. A big part of this role is setting the patterns others follow.

Требования

• Mid-level or senior experience building production LLM or agent systems, including tool/function calling, structured output, prompts, and streaming, and a working knowledge of how each one fails
• Strong systems engineering in Python (or a comparable backend language) with sound architectural judgment
• Rigorous testing discipline: dependency injection, boundary mocks, and tests that assert behavior rather than implementation
• Experience building evaluation pipelines or quality gates for model and prompt changes
• A mentoring instinct and a track record of raising the quality of the engineers around you
• Based in the UK or Germany and comfortable working fully remote
• Будет плюсом: Hands-on Anthropic API experience: Claude tool use, the Managed Agents API, MCP, or Skills; Familiarity with actor or agent frameworks and columnar analytics tools like DuckDB; Background in finance, accounting, audit, or another regulated domain

Условия

You'll join a small, senior engineering group inside a firm with deep finance and accounting expertise that's now turning that expertise into software. The bar is production quality, not prototypes, and the problems are the ones most teams skip: correctness, reliability, and cost at scale. If you want your judgment to shape how an agent platform gets built, and to see that work run against real financial workflows, this one is worth a conversation.