The Role

AI is no longer a roadmap item — it's the operating system for how modern growth teams win. We're hiring a AI Native Engineer, Growth Marketing to be the hands-on builder who embeds AI into every corner of how our Growth team works: performance marketing, creative production, CRM, landing pages, growth ops, and beyond.

This is a rare hybrid role. You're equal parts engineer and growth practitioner — someone who can write a Python script in the morning, ship a Braze-connected AI workflow by afternoon, and explain the business impact to a non-technical stakeholder by EOD. You don't need to have done this at scale before — you need to be obsessed with building at the frontier of what's possible and have the technical chops to make it real.

You'll report directly to the Head of Growth and work across every subteam in the Growth org as their embedded AI engineer.

Responsibilities:

• Performance Marketing: Build AI-powered tools to automate bid strategy analysis, budget pacing alerts, creative performance scoring, and anomaly detection across paid channels (SEM, Meta, Affiliate, YouTube, etc.)
• Creative Studio: Develop AI workflows for ad copy generation, creative brief automation, image asset variation at scale, and creative performance feedback loops between analytics and the studio team
• CRM & Lifecycle: Implement AI-driven personalization and send-time optimization across email, SMS, push, and in-app channels; build agentic workflows that pull Snowflake data, generate segment logic, and trigger Braze campaigns with minimal manual intervention
• Landing Pages & Conversion: Automate LP variant generation, headline testing ideation, and personalization logic by audience segment or traffic source
• Growth Ops: Build internal AI tools — dashboards, Slack bots, report generators — that reduce repetitive analytical work and accelerate decision-making across the team
• Growth Product: Partner with Product and Engineering to identify where AI-powered features (recommendations, dynamic content, smart nudges) can accelerate funnel conversion and retention
• Tooling & Infrastructure: Evaluate, implement, and maintain the AI/ML toolstack that powers Growth — from LLM APIs to workflow orchestration tools to data pipelines

What Success Looks Like

• A measurable reduction in manual hours across mundane Growth through AI-driven automation
• At least 5-10 production-grade AI tools or workflows live and actively used by the team within the first 4 months
• CRM campaigns that are more personalized, faster to launch, and higher-performing because of AI assist
• Creative output volume increases without a proportional increase in headcount
• The Growth team thinks of you first when they want to do something smarter, faster, or at scale

Qualifications:

• 2–5 years of engineering or technical experience, with meaningful hands-on work applying AI/LLMs to real business problems (side projects count — we care about what you've built, not just where)
• Proficiency in Python or Typescript and comfort working with APIs (OpenAI, Anthropic, or similar LLM providers)
• Build repeatable skills or plugins for popular LLM clients to empower your team
• Educate and mentor team members to become more AI-Native
• A builder's mentality: you'd rather ship a working prototype than write a spec about one
• Ability to work cross-functionally and translate between technical and non-technical stakeholders

Preferred requirements:

• Experience with workflow orchestration tools (Agent SDK, n8n, Make, Zapier, or custom Typescript/Python pipelines)
• Prior work at a DTC subscription company or growth-stage startup
• Familiarity with prompt engineering, RAG architectures, or fine-tuning
• Experience building internal tools used by non-technical teams
• Familiarity with marketing platforms and data infrastructure — ideally some combination of Braze, Google Ads, Meta Ads Manager, Snowflake, or equivalent
• Strong enough understanding of growth marketing to ask the right questions and build tools that practitioners actually want to use

Technologies You'll Use

• LLM APIs: OpenAI, Anthropic, and/or open-source models
• CRM & Marketing: Braze, Google Ads, Meta Ads Manager
• Data: Snowflake, SQL, Tableau, dbt, Hex
• Orchestration: Typescript, Agent SDK, Vercel AI SDK, Python, n8n, Make, or similar
• Collaboration: Slack, Notion, Jira, Google Workspace

Benefits

 🩺 Health Insurance coverage (Canada Life)

⛱ Flexible Paid Time Off

🗓️ 5- year Sabbatical: After 5 years with CookUnity, you get a 4-week paid sabbatical

🐣 Paid Family leave.

🕯 Compassionate Leave: 3-5 days each time the need arises

🥘 A generous amount of CookUnity credits to enjoy our amazing meals, added to your account, monthly.

🧘🏽‍♀️ Wellness perks: fitness subsidies to build a healthy lifestyle

👩🏾‍🏫 Personalized Spanish coach

🚀 Awesome opportunity to join a company that is looking to change how we eat and how chefs work!

About the Role

As a Context Engineer at CapIntel, you'll sit at the intersection of AI infrastructure and engineering. You will be responsible for how large language models are integrated into our core platform and how our engineering team adopts agentic workflows. This is a hands-on, production-focused role, not a research one. You'll build the systems that make our AI features reliable, accurate, and scalable for the wealth management enterprises that depend on us.

You'll be embedded in development teams working closely with engineers, product managers, and domain experts across the organization to design and deliver LLM-powered capabilities that directly enhance the advisor and client experience. As one of the first practitioners in this discipline at CapIntel, you'll also help define what context engineering looks like here: setting patterns and practices the broader team can build on.

This role is ideal for someone who thinks in systems, cares about production reliability over demo-day performance, and is energized by working in a discipline that is evolving quickly.

What You'll Do

• Design and implement LLM-powered features into our core application via model APIs (e.g. Anthropic, OpenAI, Cohere), with a focus on reliability and production-readiness
• Architect and maintain retrieval-augmented generation (RAG) pipelines, connecting language models to internal knowledge bases, databases, and live data sources
• Manage context window strategy, determining what information enters the model, when, in what format, and at what level of compression to optimise for accuracy, cost, and latency
• Design and implement agentic workflows enabling the platform to handle multi-step, autonomous tasks
• Build guardrail and output validation layers that constrain model behaviour and ensure AI features act within well-defined, compliant boundaries
• Develop reusable agent primitives, prompt templates, and workflow components that other engineers can build on independently
• Build evaluation frameworks to measure context effectiveness, output quality, and agent reliability in production
• Monitor deployed AI systems for failure patterns and implement mitigation strategies, feeding learnings back into continuous improvement cycles
• Collaborate with Product, Product Engineering, Implementation, and Data teams to translate business requirements, and proof of concepts into production AI system specifications
• Act as an internal practitioner and resource helping upskill the broader engineering team on context engineering principles and agentic best practices

What We're Looking For

• 5+ years of professional software engineering experience, with at least 1–2 years working with LLMs in a production context
• Strong experience with Python or Node and building API-integrated backend services
• Hands-on experience with an orchestration or execution framework
• Working knowledge of RAG architecture, vector databases (e.g. Pinecone, pgVector, AWS OpenSearch), and semantic search
• Familiarity with context management techniques: summarisation, chunking, session splitting, and memory strategies
• Experience building or consuming REST APIs and integrating with third-party services
• Comfortable collaborating with cross-functional teams in a fast-paced, high-growth environment
• Strong problem-solving instincts and a willingness to learn and adapt as the field evolves

Nice to Have

• Experience with the Model Context Protocol (MCP) or similar tool-integration standards
• Familiarity with LLMOps practices: tracing, observability (e.g. LangSmith, Datadog), and model versioning
• Exposure to multi-agent architectures and orchestration patterns
• Knowledge of AI output validation, context safety, and governance considerations particularly relevant in regulated industries like financial services
• Familiarity with AWS or cloud-based infrastructure and containerised deployments (Docker, Kubernetes)
• Ability to communicate technical concepts clearly to both technical and non-technical partners