Reports to:
Director, Engineering
Engineering Organization & Culture
At Bullish, we are engineering the institutional standard for the digital asset industry. Our mission is to build a platform where security and compliance are the foundational core, requiring a commitment to technical excellence that goes beyond simply delivering code. We operate as a global engineering organization, setting a high bar in a demanding environment for those driven to do the best work of their careers alongside world-class peers.
We value engineers who treat development as a craft and own the outcome from concept to deployment. You will be expected to navigate the unknown, bring structure to ambiguity, and help shape the frameworks and processes that drive our global teams forward. We refuse to compromise on quality and seek problem solvers who thrive on high-impact technical challenges.
The Team: AI & Data Platform
The AI & Data Platform team powers intelligence across the Bullish ecosystem—from our institutional-grade cryptocurrency exchange to CoinDesk's media, data, and indices businesses. We build the infrastructure that transforms raw data into governed, trustworthy assets and deploy AI systems that meet the reliability standards our institutional clients expect.
We operate at the intersection of data engineering, semantic modeling, and applied AI—delivering solutions across the enterprise, spanning the full breadth of Bullish operations from trading floors to compliance.
As we expand our AI capabilities, we are building out a robust evaluation and governance layer—ensuring every AI system we deploy is observable, testable, and held to the same engineering standards as our trading infrastructure. This is a team where AI is treated as serious engineering, not experimentation.
What You'll Do
AI Systems Architecture: Design and implement production AI systems with emphasis on reliability, observability, and continuous evaluation. Champion engineering practices that ensure AI outputs are consistent, evidence-based, and auditable.
Conversational Analytics: Lead development of natural-language interfaces to business data, enabling stakeholders to query complex datasets through governed semantic layers.
Agent Development: Architect multi-agent systems that coordinate across data sources, applying industry best practices for agent orchestration, tool use, and structured output generation.
Evaluation & Trust: Build evaluation harnesses and testing frameworks that measure AI system quality—including groundedness, factual consistency, and output reliability—before deployment to production.
Cross-Functional Collaboration: Work closely with product, trading, research, and media teams to translate complex requirements into scalable AI solutions with clear success metrics.
Technical Leadership: Mentor engineers, establish coding standards, and drive architectural decisions that balance innovation velocity with production stability.
Semantic Layer Partnership: Partner with data engineering to define and enforce semantic models that bridge raw data to AI-consumable formats, ensuring business logic is captured once and reused across applications.
What You'll Bring
AI Engineering Experience: 5+ years building production AI/ML systems, with demonstrated experience deploying LLM-based applications beyond proof-of-concept.
Agent & Orchestration Expertise: Hands-on experience with agent frameworks, tool-use patterns, and multi-step reasoning systems.
Experience with at least 3 of the following:
- LangChain / LangGraph (agent orchestration, stateful workflows)
- LlamaIndex (RAG pipelines, data indexing, query engines)
- Multi-agent frameworks (CrewAI, AutoGen/AG2, MetaGPT, OpenAI Agents SDK)
- Model Context Protocol (MCP) for tool connectivity
- DSPy (programmatic prompting, optimization)
- Vector databases (Qdrant, Pinecone, ChromaDB, Weaviate, pgvector)
- Structured output libraries (Instructor, Pydantic, Zod)
- LLM inference infrastructure (vLLM, Ollama, TensorRT-LLM)
- Cloud AI platforms (Vertex AI, Amazon Bedrock, Azure OpenAI)
- Evaluation & observability tools (LangSmith, Braintrust, Weights & Biases, Arize)
Data Platform Fluency: Strong background in data engineering, semantic modeling, or analytics infrastructure. Comfortable navigating data lakes, governance tools, and BI systems.
Full-Stack Capability: Proficiency across the stack—Python for AI/ML, cloud infrastructure (GCP preferred), and exposure to frontend integration for conversational interfaces.
Engineering Rigor: Track record of building observable, testable, well-documented systems. Experience with CI/CD for ML, experiment tracking, and model governance.
Communication: Ability to translate between technical implementation and business value. Comfortable presenting to senior stakeholders and aligning diverse teams.
Nice to Haves
Experience in financial services, fintech, or digital asset sectors
Background in explainable AI or behavioural evaluation methodology
Familiarity with enterprise data governance and metadata management
Experience building conversational or natural-language interfaces to structured data
Knowledge of market data, trading systems, or financial analytics
Bullish is proud to be an equal opportunity employer. We are fast evolving and striving towards being a globally-diverse community. With integrity at our core, our success is driven by a talented team of individuals and the different perspectives they are encouraged to bring to work every day.
