Position Overview We are seeking a Senior Databricks ML Engineer to support the delivery of an AI/analytics application by evaluating, integrating, and/or redeveloping machine learning models within a Databricks environment. This role joins after the discovery phase and becomes a key contributor to ensuring ML models are production-ready , aligned to the MVP experience , and successfully integrated into the broader data and application workflow. You will work closely with a lead architect and data engineering team to assess the current state of models (if they exist), determine the best path forward (reuse, tune, rebuild), and deliver a tightly scoped MVP focused on a limited asset set. Key Responsibilities Model Evaluation & Strategy Assess existing ML models for accuracy, reliability, maintainability, and production readiness. Evaluate compatibility with Databricks workflows and data pipeline patterns. Recommend and document the best approach per model: reuse as-is , fine-tune , or rebuild . Define success criteria aligned to MVP needs (performance, latency expectations, output format, explainability where required). Model Development, Retraining & Tuning Develop new models or retrain existing models based on available data and MVP requirements. Fine-tune models and iterate based on validation results and stakeholder feedback. Implement feature engineering logic and ensure training/serving consistency. Ensure models meet agreed performance expectations for MVP delivery. Databricks ML Integration Integrate model training and inference into Databricks pipelines and jobs. Partner with data engineers to confirm data availability, quality checks, and feature inputs. Support repeatable execution patterns (notebooks, jobs/workflows) and maintain clear documentation. Application / Output Integration Enable model outputs to feed the application/UI layer in the required format and cadence. Ensure outputs align to the user-facing experience demonstrated during MVP design (e.g., output fields, scoring, ranking, flags). Support near-real-time or batch execution patterns as required by the MVP scope. Testing, Validation & Delivery Validate model outputs against business expectations and known scenarios. Support benchmarking, drift checks (where applicable), and performance validation. Partner with QA (if present) and contribute to test coverage for ML-related functionality (data checks, output sanity checks, regression validation). Communicate progress, risks, and tradeoffs clearly in a consulting delivery environment. Required Experience & Qualifications Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience. 5+ years of experience in machine learning engineering, data science, or applied ML roles with production exposure. Strong hands-on experience with Databricks and distributed processing using PySpark . Strong proficiency in Python for model development, evaluation, and integration. Demonstrated experience in: Evaluating existing models and determining reuse vs. tuning vs. rebuild strategy Retraining, optimization, and performance validation Productionizing ML models in enterprise environments (reliable execution, reproducibility, monitoring/validation mindset) Ability to work effectively in ambiguous environments with incomplete inputs and evolving requirements. Strong communication skills and ability to collaborate with architects, data engineers, and application stakeholders. Preferred Qualifications Experience with sensor/time-series/operational data and feature engineering patterns for these domains. Exposure to industrial or asset-based analytics use cases (predictive maintenance, anomaly detection, asset health scoring, etc.). Familiarity with ML lifecycle tooling in Databricks (e.g., MLflow ), model governance concepts, or model registry patterns. Experience supporting incremental scoring, near-real-time analysis, or streaming-adjacent pipelines (conceptual or hands-on). Databricks or cloud certifications (or active pursuit).