• You will help improve our existing ML-based products and build new features and services using LLMs. • A core part of the role is context engineering — designing the right inputs, retrieval strategies, and prompt structures to get the best possible output from LLMs in production. • You'll also contribute to our software engineering efforts when needed, bridging the gap between ML and software engineering on the team. • You will work in the Machine Learning Team, where we build LanguageWire's machine translation (MT) and adjacent services to monitor and predict its performance. • The team consists of ML Engineers, Analytics Engineers, and Full Stack Software Engineers. • You'll collaborate across these disciplines daily, and we value people who can move fluidly between ML experimentation and production engineering. • You'll report to the team leader.

• Build and ship AI features end-to-end (model → system → user experience) • Design and iterate on prompts, tools, memory, and agent workflows • Turn raw model outputs into structured, reliable, and predictable behaviors • Debug issues across the full stack (model, orchestration, infra, UX) • Optimize for latency, cost, and production reliability • Develop lightweight evaluation frameworks to measure real-world performance • Work closely with product and engineering to translate ambiguous problems into working systems

• Build and ship AI features end-to-end (model → system → user experience) • Design and iterate on prompts, tools, memory, and agent workflows • Turn raw model outputs into structured, reliable, and predictable behaviors • Debug issues across the full stack (model, orchestration, infra, UX) • Optimize for latency, cost, and production reliability • Develop lightweight evaluation frameworks to measure real-world performance • Work closely with product and engineering to translate ambiguous problems into working systems

• Build AI-powered infrastructure that gives VC and PE clients a genuine edge — proprietary systems that extract signals, automate decisions, and compound in value over time. • Agentic AI Systems: Multi-step LLM workflows, RAG pipelines, and agent orchestration systems — owned from architecture to production. • Full-Stack AI Applications: Client-facing web applications with AI embedded throughout — Python/FastAPI backends, React frontends, integrated with LLM providers (OpenAI, Anthropic, Gemini). • Data Platform Engineering: Scalable pipelines and cloud infrastructure (AWS/GCP) that underpin AI features — vector databases, data ingestion layers, API integrations. • Technical Discovery & Client Engagement: Translating business needs into AI-first technical proposals. • AI Quality & Internal Standards: Guardrails, automated testing, and observability for AI systems.