AI Machine Learning Engineer

About Grower

GrowerAI is a Marketing AI Operating System, not another marketing tool, but the platform that connects them all. We're building an OS-level solution where intelligent automation, predictive analytics, and AI-powered decision-making replace the chaos of disconnected point solutions.

Think of us as the operating system for marketing operations: unified workflows, centralized intelligence, and seamless orchestration across channels, tools, and teams. Our enterprise clients use Grower as their command center for running sophisticated marketing operations at scale.

The Role

You'll build the intelligence layer that powers our platform's AI capabilities. This means creating production ML systems, integrating LLMs for strategic insights, building recommendation engines, and developing predictive models that help marketers make data-driven decisions.

You'll work directly with Product and Engineering to ship AI features that impact thousands of daily platform interactions, moving from user story to deployed model in weeks, not quarters.

What You'll Build

LLM & Generative AI Integration

• Build RAG (Retrieval-Augmented Generation) pipelines for context-aware recommendations
• Integrate OpenAI, Anthropic, or open-source LLMs into product features
• Design prompt engineering systems and agent-based workflows
• Implement semantic search and embedding-based matching

Predictive & Optimization Systems

• Forecasting models for budget planning and performance prediction
• Recommendation algorithms for resource allocation across channels
• Anomaly detection systems for real-time monitoring
• Multi-source data reconciliation and attribution modeling

AI-Powered Features

• Strategic planning assistants who analyze data and generate insights
• Automated report generation with natural language summaries
• Smart routing and workflow automation based on user behavior
• Conversational AI for in-platform guidance

Data & Infrastructure

• Feature engineering pipelines from multiple API sources
• Real-time inference endpoints for in-platform AI features
• Model monitoring, evaluation, and retraining automation
• Vector databases and semantic search infrastructure

Required Experience

AI & LLM Experience (Critical)

• 2–4 years building production AI/ML systems
• Hands-on experience with LLMs: OpenAI API, Anthropic Claude, or open-source models
• Experience with embeddings, vector databases (Pinecone, Weaviate, ChromaDB or others)
• Understanding of prompt engineering, fine-tuning, or RAG architectures
• Experience building conversational AI or agent-based systems

Core ML Skills

• Strong Python: NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow
• Experience with supervised/unsupervised learning and recommendation systems
• Time series forecasting and predictive modeling
• Track record of moving models from development to production

Engineering Fundamentals

• Comfortable working with REST APIs and microservices
• Experience with data pipelines and ETL processes
• Git, Docker, basic CI/CD knowledge
• SQL proficiency for data manipulation

Highly Valued

• Experience working with advertising platform APIs (Meta, Google, TikTok, etc.)
• Familiarity with analytics tools and data integration patterns
• Previous work in fast-moving startups or SaaS companies
• Experience with AWS/GCP ML services (SageMaker, Vertex AI, Bedrock)
• Understanding of A/B testing and experimentation frameworks
• Experience with LangChain, LlamaIndex, or similar LLM frameworks

Success in First 6 Months

• Ship 2+ production AI features that directly impact user experience
• Build and deploy at least one LLM-powered capability
• Establish monitoring dashboards for model performance and data quality
• Contribute to AI roadmap planning with the Product team
• Create reusable AI components and best practices

What Makes This Role Different

AI-First: You'll work on cutting-edge LLM integration, not just traditional ML. Expect to build agents, RAG systems, and conversational interfaces.

Speed: We operate on aggressive timelines (Q1-Q2 2026 delivery targets). You'll ship fast and iterate based on real user feedback.

Cross-Functional: You'll work closely with PMs who write detailed user stories, designers who shape AI experiences, and engineers who build the platform around your models.

Ownership: This isn't a research role; you own features end-to-end, from problem definition through production deployment and monitoring.

Learning Context: You'll need to quickly understand how the platform works and what data means in our context. We'll teach you the domain; you bring the AI expertise.

Our Stack & Approach

• Python for ML, microservices architecture for deployment
• LLM APIs and embedding models for AI features
• Integration with multiple third-party APIs for data sources
• Real-time analytics and data processing pipelines
• Confluence for documentation, Figma for design collaboration, Jira for development workflow