Kalibri is looking to redefine and rebuild the hotel industry by harnessing cloud-native data pipelines with advanced AI/ML models to drive asset performance and increase asset values.
Requirements
- Proven track record building and scaling ML platforms and taking models from research to production.
- Deep experience with MLOps best practices: model lifecycle management, orchestration (Prefect strongly preferred, Airflow or similar acceptable), automated retraining, and monitoring in production.
- Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow) and in orchestrating workloads on Kubernetes.
- Expertise with cloud-native environments (AWS preferred), including services like S3, Lambda, Step Functions, and IAM.
- Familiarity with data warehouses and ELT pipelines (Snowflake, dbt) to support ML feature pipelines.
- Experience deploying and optimizing LLMs or other advanced generative AI models in production.
- Knowledge of vector databases, semantic search, and retrieval-augmented generation (RAG) pipelines.
Responsibilities
- Define and own the long-term strategy for Kalibri’s ML platform, enabling rapid prototyping, efficient training, and reliable production deployment of models.
- Partner with Data Science to design and implement end-to-end ML workflows, including data ingestion, feature engineering, model training, evaluation, and serving.
- Lead the development and operation of scalable, cloud-native ML infrastructure using Prefect, Snowflake, DBT, and modern MLOps tooling.
- Implement best practices for CI/CD, model versioning, experiment tracking, automated testing, and monitoring of ML systems.
- Build, mentor, and grow a high-performing ML engineering team; foster a culture of collaboration, learning, and operational excellence.
- Ensure production models meet SLAs for accuracy, latency, and availability. Implement and deploy continuous evaluation for drift and degradation.
- Evaluate, select, and integrate modern AI/ML tools, frameworks, and services (e.g., PyTorch, TensorFlow, vector databases, LLM frameworks) to accelerate the roadmap.
Other
- 10+ years in software engineering, data engineering, or ML engineering roles, including 3+ years in a technical leadership role at the Director+ level.
- Strong leadership skills with experience hiring, mentoring, and retaining technical talent.
- Excellent cross-functional collaboration skills with proven ability to align data scientists, engineers, and product managers around shared outcomes.
- Demonstrated ability to deliver complex technical projects in SaaS environments.
- Experience in a startup environment.
