Design and build production-ready AI/ML systems, with an emphasis on Standard Model LLM-powered product and platform features
Leverage LLM tooling/APIs such as LangChain and MCP connectors to implement retrieval-augmented generation (RAG), copilot-assistants and agentic workflows
Partner with Product Management and product teams to translate requirements into AI-powered capabilities that surface directly in user-facing products
Apply MLOps and LLMOps best practices: monitoring, evaluation, prompt versioning, cost/performance optimization
Combine traditional AI/ML with modern GenAI approaches to deliver hybrid solutions where appropriate
Collaborate with Data Engineers to establish a scalable data pipeline that serves structured data shaped for LLM consumption, feature store data for traditional AI, and Trad/GenAI-enhanced insights for internal and customer-facing BI use cases
Mentor and upskill peers in on core AI/ML and LLMOps practices, raising the overall AI/ML competency of the team
Stay current with developments in GenAI, LLMOps, generative AI safety frameworks, and evaluate their potential for adoption within the platform

7+ years of professional software engineering experience
3+ years experience in traditional AI/ML
1+ year experience in building LLM applications on standard models
Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field (or equivalent experience)
Hands-on familiarity with Prompt Engineering by leveraging LLM frameworks such as LangChain
Strong programming skills in Python
Solid understanding of data engineering practices (ETL/ELT, streaming, orchestration with Airflow/Temporal, dbt, Kafka, etc.)
Knowledge of LLMOps/MLOps practices: CI/CD for ML, model monitoring, drift detection, evaluation metrics, governance
Strong collaboration and communication skills
Demonstrated ability to mentor and upskill engineers, particularly in data/ML workflows
Skilled in designing/building/deploying/operating LLM standard model-powered features in production
Proficient in working with traditional cloud AI/ML platforms such as Amazon Sagemaker, GCP Vertex AI, or Azure ML and frameworks such as TensorFlow, PyTorch, scikit-learn.

Senior AI Engineer

Key skills