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We are seeking a high-impact ML/Data Engineer to join our team. In this role, you won't just write theoretical reports or run isolated experiments—you will embed directly into business environments to design, build, and deploy production-grade data pipelines, models, and AI systems that drive real, measurable metrics. You will thrive in this position if you love end-to-end ownership, enjoy moving quickly across messy datasets, and leverage AI tools in your daily workflow to multiply your output.

End-to-End System Ownership: Own the entire lifecycle of data solutions—from discovery and ETL pipeline architecture to model integration, evaluation, and delivery. Rapid Domain Navigation: Walk into diverse business domains, quickly understand complex data landscapes, and identify high-leverage opportunities for AI/ML. Production Integration: Build robust data pipelines and integrate ML models and LLMs directly into production systems that real users depend on. Cross-Functional Communication: Partner directly with business stakeholders to run data discovery, articulate what data can and cannot do, and translate needs into technical specs. AI-Augmented Execution: Actively use LLM APIs, agents, and coding assistants within your own workflow to ship working solutions fast.

Data Engineering & ETL: Strong hands-on experience working with messy, real-world data and building reliable data pipelines. Core ML & Data Stack: High fluency with Python, SQL, pandas, and at least one core ML framework. Production Experience: Proven background deploying ML models or LLM integrations into live production environments. Technical Scope & Speed: Ability to quickly assess feasibility and scope technical solutions without needing months of ramp-up time. Pragmatic Delivery Mindset: A strong bias toward shipping functional software over endlessly tweaking for theoretical optimality. Nice to Have Hands-on experience with vector databases, embeddings, or RAG architectures in production. Experience fine-tuning or evaluating LLMs for domain-specific business use cases. Prior experience working across multiple industries (e.g., healthcare, fintech, logistics, consumer). Public repos, notebooks, or technical writing showcasing your approach to building with data and models.