Job Brief:
- Role Objective
Ensure all production machine learning models are reliable, monitored, retrainable, and auditable. This role exists to prevent silent model decay and reduce operational load on Data Science teams by architecting robust automated deployment and monitoring frameworks.
- Role Description
The Senior MLOps Engineer handles the technical implementation of production-grade Machine Learning systems. This involves architecting scalable pipelines, managing real-time inference services, and ensuring robust integration within cloud environments. The focus is on the transition of models from experimentation to high-throughput services, ensuring every solution is high-performing, secure, and engineered to solve complex organizational challenges at scale.

VentureDive Overview:
Founded in 2012 by veteran technology entrepreneurs from MIT and Stanford, VentureDive is the fastest-growing technology company in the region that develops and invests in products and solutions that simplify and improve the lives of people worldwide. We aspire to create a technology organization and an entrepreneurial ecosystem in the region that is recognized as second to none in the world.

Key Responsibilities:

• End-to-End Productionization: Architect and deploy ML models through the full lifecycle (training → deployment → monitoring).
• Model Observability: Implement comprehensive monitoring for Data Drift, Model Drift, and Performance Degradation.
• Retraining & Rollbacks: Own automated retraining pipelines and safe rollback mechanisms to maintain model integrity.
• ML CI/CD: Build and maintain automated workflows for model versioning, lineage, and artifact storage.
• Inference Optimization: Implement model optimization techniques such as Quantization, Pruning, and Caching to reduce compute costs and improve latency.
• Standardization: Establish practices for model registries and metadata tracking to ensure all production assets are auditable and reproducible.
• Operational Integration: Partner with teams to operationalize models used for Search Ranking, Valuation Engines, and Quality Scoring.

Technical Stack & Tools

• Programming: Python (Expert), SQL (Expert), and FastAPI or Flask (for Model APIs).
• Orchestration: Airflow, Dagster, or Prefect; GitHub Actions for CI/CD.
• MLOps Frameworks: MLflow, DVC, or Weights & Biases; Kubeflow.
• Infrastructure: Docker, Kubernetes.
• Cloud Platforms: AWS SageMaker, Azure ML, or GCP Vertex AI.
• Monitoring: Prometheus, Grafana, or any specialized tool.
• Data Processing: Apache Spark (PySpark), Databricks, or Snowflake.

Qualifications & Experience

• Professional Experience: 5+ years in Data Science/ML and 3+ years specifically in MLOps / Production Engineering.
• Production Delivery: Proven track record of building, deploying, and maintaining high-throughput inference services in live environments.
• System Grit: Comfortable working with legacy systems and imperfect infrastructure to architect robust data features and pipelines.
• Business Systems Integration: Experience engineering ML solutions that consume data from core systems such as ERP, CRM, or Supply Chain modules.
• Cloud Collaboration: Extensive experience implementing AI solutions via Cloud (Azure, AWS, or GCP) while collaborating with Data Engineering and DevOps teams.

What we look for beyond required skills
In order to thrive at VentureDive, you …are intellectually smart and curious …have the passion for and take pride in your work …deeply believe in VentureDive’s mission, vision, and values …have a no-frills attitude …are a collaborative team player …are ethical and honest

Are you ready to put your ideas into products and solutions that will be used by millions? You will find VentureDive to be a quick pace, high standards, fun and a rewarding place to work at. Not only will your work reach millions of users world-wide, you will also be rewarded with competitive salaries and benefits. If you think you have what it takes to be a VenDian, come join us ... we're having a ball!

#LI-Hybrid