Software – Senior AI & Data Engineer

Job Description

Key Responsibilities :

– Agentic AI Design & Implementation : Architect, develop, and deploy AI agents and LLM-powered solutions for our BD and M&A teams using frontier models (e.g., OpenAI, Anthropic Claude, Google Gemini), including context engineering, tool use, structured outputs, and integration with enterprise systems.

– Data Architecture & Reporting : Work across our data landscape (databases, data lakes, and warehouses such as Snowflake) to ensure clean, well-modeled, and accessible data. Bring AI into our modern reporting architecture, enhancing Tableau dashboards and analytics workflows with AI-driven insights, natural language querying, and automated data preparation.

– Automation & Integration Workflows : Design, build, and maintain workflow automations using n8n and agent frameworks to connect AI models, MCP servers, APIs, and enterprise systems (e.g., Salesforce, Snowflake, Google Workspace, Slack). Optimize process automation and ensure reliability, scalability, and error handling across integrations.

– Evaluation, Optimization & Governance : Build evals to measure quality, accuracy, and cost. Continuously monitor performance, implement guardrails, and ensure AI systems remain accurate, compliant, and efficient in production.

– AI Application Development : Analyze data and develop bespoke AI applications, including retrieval-augmented generation (RAG) pipelines and data enrichment services, that enhance decision-making and automate complex business processes.

– Hands-on Software Development : Make significant code contributions, accelerated by agentic coding tools like Claude Code: setting up architectural scaffolding, CI/CD pipelines, and infrastructure-as-code, and solving complex, blocking integration challenges for the team.

– Collaboration : Partner effectively with cross-functional teams, champion responsible AI adoption across the business, and contribute to a culture of innovation and continuous improvement.

Required Qualifications & Experience :

– Deep AI Understanding : Excellent grasp of how LLMs, AI agents, and AI automation work: model capabilities and limitations, agentic reasoning and tool use, and where AI creates genuine business value. This understanding matters more to us than any single tool.

– Applied LLM & Agentic Experience : Practical experience using language models for tasks such as summarization, information extraction, classification, and data enrichment, and connecting those capabilities to real-world business applications through API integrations, webhooks, and error-handling logic. Demonstrated ability to deploy frontier models (e.g., OpenAI, Anthropic, Google) into production. We use n8n and Python as our primary tools, but what matters is your ability to leverage whichever tools the problem calls for.

– Data Foundations : Strong understanding of modern data architecture: relational databases, data lakes, and cloud warehouses (e.g., Snowflake), including data modeling, pipelines, and SQL. Comfortable working with APIs and whatever data tools the job requires.

– Reporting & Analytics : Experience with modern reporting and BI, ideally Tableau, and an informed view of how AI fits within today’s reporting architecture: AI-assisted analysis, natural language interfaces to data, and automated insight generation.

– Context Engineering & Evaluation : Creative and analytical approach to crafting, testing, and refining prompts and context strategies. Ability to systematically evaluate AI outputs and iterate to improve quality, accuracy, and performance.

– AI-Native Development : Fluency with agentic coding tools, especially Claude Code (Cursor and GitHub Copilot also count), as a core part of how you build: delegating well-scoped tasks to coding agents, reviewing their output critically, and multiplying your own productivity.

– Architectural Understanding : Solid understanding of modern architectural patterns (microservices, serverless, event-driven architecture) and the principles of Domain-Driven Design (DDD).

– Domain Knowledge : Understanding of financial data, company performance, and research processes to support AI-driven initiatives across M&A and BD workflows.

– Communication & Interpersonal Skills : Exceptional communication, presentation, and listening skills. Ability to articulate complex technical issues and solutions to both technical and non-technical stakeholders effectively, along with clear written documentation.

– Education : Bachelor’s degree in Computer Science, Data Science, or a related field preferred.

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