Drive innovation in Generative AI by designing, building, and deploying LLM-powered solutions that directly impact products and users.
Requirements
- 3+ years applied machine learning, with hands-on focus on NLP, transformers, or generative AI systems.
- Hands-on experience with LLM-related libraries (e.g. LangChain, LlamaIndex, OpenAI API, CrewAI, or similar) and services (Azure Prompt flow, AWS Bedrock agents, or similar)
- Experience designing multi-step agents that combine LLM reasoning with tool/API calls, with safeguards against errors, loops, and unsafe tool use.
- Proven experience building and deploying machine learning models to production (API, batch, or streaming).
- Fluency in Python, with clean, modular, production-grade code practices.
- Strong ability to design and analyze ML experiments; track performance using metrics, not gut feel.
- Ability to develop, deploy and monitor AI-powered applications in cloud environments (e.g. AWS, Azure, GCP) using APIs, batch, or streaming architectures. Familiarity with containerization, versioning, and CI/CD.
Responsibilities
- Architect and implement production-ready AI solutions involving LLMs, transformer-based models, retrieval systems, agentic workflows, and AI agents for generative tasks and automation.
- Design and iterate on prompts, workflows, and RAG pipelines to improve accuracy, cost-efficiency, latency, and safety.
- Design and build multi-step agentic systems that break down complex tasks, invoke external tools or APIs, manage state, and handle reasoning chains robustly.
- Deploy models and GenAI pipelines in production environments (API, batch, streaming), ensuring reliability and scalability.
- Build and maintain evaluation frameworks to measure model grounding, factuality, latency, and cost.
- Develop and integrate guardrails (e.g., prompt-injection protections, content moderation, output validation), and safeguards for agent loops (e.g., loop prevention, tool call limits, state validation).
- Collaborate cross-functionally with Product, Engineering, and ML Ops to deliver high-quality AI features end-to-end.
Other
- Degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience).
- Ability to bridge rapid prototyping and production deployment — you own what you build through to live systems.
- Strong collaborator who thrives at the intersection of DS + Engineering.
