Key Facts

Remote From:

India

Category: Field Engineer (Solutions)

Full time

Senior (5-10 years)

English

Hard Skills

Databricks MLflow Python (Programming Language) Mload Reinforcement Learning SQL (Programming Language) CI/CD Camera Calibration Kubeflow Apache Airflow +2 more

Other Skills

•
Collaboration

Roles & Responsibilities

7+ years in MLOps, ML Engineering, or related roles
Proficient in using Databricks, Apache Spark, ML Flow, Unity Catalog, and feature stores
Expertise in deploying low-latency ML models and reinforcement learning solutions
Strong knowledge of Git workflows and CI/CD practices

Requirements:

Build and maintain scalable ML infrastructure on Databricks
Design and implement frameworks for detecting data and model drift
Develop model calibration frameworks and establish versioning practices
Create automated frameworks for training, retraining, and validating ML models

MDA Edge

About MDA Edge

Our objective is to establish an exceptional ecosystem by connecting individuals, technology, and prospects through the application of human intelligence.MDA Edge is a process-oriented company and our expertise lies in providing comprehensive Workforce Solutions, specifically focusing on Contingent Staffing, Bulk/Project Staffing, RPO/KPO/BPO, and Direct Hire services. We cater to a wide range of industries, including Infrastructure Consulting, Engineering Consulting, IT Consulting, Healthcare, Life Sciences, Pharmaceutical Consulting, Consumer Goods, Education, Transportation & Logistics, Media & Entertainment, Telecom, BFSI, Manufacturing, Utilities & Energies, and Corporate Recruitment. Moreover, we have developed a dedicated focus on Government Consulting.Our company prides itself on a meticulous approach, ensuring that we meet your specific needs. We have established a robust network of highly skilled professionals who are readily available to fulfill your requirements. With a strong commitment to excellence, we consistently deliver exceptional results for clients in India, Denmark, France, Germany, Ireland, Japan, Spain, Thailand, the USA, Canada and Mexico, with ongoing expansion efforts to serve more new regions in the near future.We consistently uphold the highest standards of quality by providing resources, time, and materials to design, implement, and support efficient operations for organizations. Our track record of measurable accomplishments demonstrates our commitment to cultivating a balanced work and societal ecosystem.Our continuous growth, successful customer engagements, and strong customer retention exemplify our achievements. Furthermore, our passion lies in streamlining complex business processes through the application of suitable technology, which has been integral to our success.We extend an invitation to join our dynamic workplace, offering rapid growth opportunities, excellent employee benefits, and a positive work-life balance.

Company type: Scaleup

Founded: 2018

Company size: 201 - 500

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Job description

Job Summary: We seek a Senior ML Ops Engineer to play a critical role in operationalizing machine learning workflows that drive dynamic pricing and personalized consumer experiences. This position focuses on building robust ML infrastructure and frameworks, including drift detection, model calibration, versioning, and reinforcement learning orchestration. The ideal candidate will bring expertise in Databricks, Unity Catalog, and feature stores and a deep understanding of Git workflows, Databricks workflows, and automated ML training pipelines.
Qualifications:

7+ years in MLOps, ML Engineering, or related roles, focusing on deploying and managing ML workflows in production environments. Hands-on experience building drift detection systems, model calibration frameworks, and robust monitoring tools for ML pipelines.
Proficient in using Databricks, Apace Spark, ML Flow, Unity Catalog, and feature stores.
Expertise in deploying and orchestrating low-latency ML models, including reinforcement learning solutions like Contextual Bandits and Q-learning.
Experience designing automated training pipelines for ML models, focusing on efficiency
Strong knowledge of Git workflows, CI/CD practices, and tools like GitLab or similar.
Proficiency in Python, SQL, and big data processing tools like Spark.
Familiarity with ML lifecycle tools such as MLflow, Kubeflow, and Airflow.
Strong understanding of model performance monitoring, drift detection, and retraining workflows.

Key Responsibilities

ML Infrastructure Development: Build and maintain scalable ML infrastructure on Databricks, leveraging Unity Catalog and feature stores to support model development and deployment.
Drift Detection Frameworks: Design and implement frameworks for detecting data and model drift, ensuring continuous monitoring and high reliability of ML models in production.
Model Calibration & Versioning: Develop model calibration frameworks and establish versioning practices to maintain transparency and reproducibility across the ML lifecycle.
Low-Latency Orchestration: Design and optimize reinforcement learning (RL) orchestration pipelines, including Contextual Bandits, for real-time execution in low-latency environments.
Automated Training Pipelines: Create automated frameworks for training, retraining, and validating ML models, enabling efficient experimentation and deployment.
CI/CD for ML: Implement CI/CD best practices to streamline the deployment and monitoring of ML models, integrating with Databricks workflows and Git-based version control systems.
Collaboration: Work closely with ML Scientists to ship, deploy, and maintain models.
Monitoring & Optimization: Build tools for model performance monitoring, operational analytics, and drift mitigation, ensuring reliable operation in production environments.