Smith is looking to build and ship AI features end-to-end, including data preparation, modeling, evaluation, deployment, and iteration, by collaborating with the engineering team to translate ideas and research into reliable, production-grade systems.
Requirements
- Strong foundation in algorithms and data structures; able to analyze time/space complexity and choose the right approach
- Solid understanding of core ML principles: bias/variance, feature engineering, cross-validation, regularization, evaluation metrics
- Familiarity with LLMs: tokenization basics, model families, fine-tuning concepts, RAG patterns, and LLM evaluations
- Exposure to agentic AI concepts: tool calling, planning, memory, and simple multi-agent orchestration
- Knowledge of Model Context Protocol (MCP) for context sharing, secure integrations, and tool orchestration
- Proficiency in Python and common libraries (NumPy, pandas, scikit-learn; plus, PyTorch or TensorFlow preferred)
- Comfort with AWS fundamentals (IAM, S3, compute/container runtimes) or equivalent cloud experience
Responsibilities
- Build LLM-powered features (prompt design, RAG pipelines, tools/plug-ins, evaluations, guardrails)
- Experiment with agentic AI patterns (tool use, planning/re-planning, multi-agent workflows) and ship reliable agents
- Implement and evaluate machine-learning models (classification, regression, clustering, NLP, CV) from prototype to production
- Write clean, well-tested Python code for data processing, modeling, and service APIs
- Package and deploy models/services on AWS (e.g., S3, Lambda, ECS/EKS, SageMaker) with basic CI/CD
- Design simple, efficient data pipelines and integrate with databases (SQL/NoSQL) and vector stores
- Monitor models in production (latency, drift, quality) and iterate based on telemetry and user feedback
Other
- Bachelor’s or master's in computer science, Data Science, EE, or related field (or equivalent projects/internships)
- 0–2 years of professional experience; internships, open-source, or notable personal projects count
- Strong ability to adapt to new technologies and rapidly learn by reading docs/papers and implementing new ideas
- Bias for action: iterate quickly, measure results, and improve based on evidence
- Clear communication and collaborative mindset; comfortable receiving and giving feedback
