We are looking for highly skilled and experienced professionals to join our team in two key roles: Machine Learning Engineer and ML Ops Engineer.
As a Machine Learning Engineer, you will play a critical role in designing, developing, deploying, and optimizing machine learning models and intelligent applications that address complex business challenges. Working closely with data scientists, software engineers, and business stakeholders, you will bridge the gap between research and production by building scalable, reliable, and high-performing ML solutions across cloud and edge environments.
As a Machine Learning Ops Engineer, you will serve as the engineering backbone of our AI delivery ecosystem, enabling Machine Learning Engineers working across Computer Vision and Generative AI initiatives to build, deploy, and operate production-grade ML systems at scale.
Key Responsibilities of Machine Leaning Engineer
Model Development & Implementation
- Design, develop, and implement machine learning models and algorithms to solve business problems
- Optimize model performance, accuracy, and efficiency through feature engineering and hyperparameter tuning
- Research and evaluate new ML techniques, frameworks, and tools
Production Systems & Infrastructure
- Deploy ML models into production environments using cloud platforms and containerization technologies
- Build and maintain scalable ML pipelines for data processing, model training, and inference
- Implement MLOps practices including model versioning, monitoring, and automated retraining
- Ensure model reliability, scalability, and performance in production systems
- Optimize models for deployment across different environments (cloud, edge)
Edge Deployment
- Deploy and optimize ML models for edge devices
- Implement model compression techniques (quantization, pruning, distillation)
- Work with edge computing frameworks and optimize for resource-constrained environments
- Ensure low-latency inference and efficient resource utilization
Data Engineering & Analysis
- Work with large-scale datasets to extract, transform, and prepare data for ML applications
- Design efficient data pipelines
- Ensure data quality and implement data validation processes
Collaboration & Communication
- Partner with data scientists, software engineers, and product teams to define requirements
- Communicate technical findings and model performance to both technical and non-technical stakeholders
- Participate in code reviews and maintain high standards for code quality
- Document models, processes, and best practices
Key Responsibilities of ML Ops Engineer
ML Pipeline Engineering
- Build and maintain reproducible ML pipelines for CV and GenAI use cases
- Convert notebook-based and ad hoc workflows into version-controlled, testable, maintainable pipeline components
- Standardize training and evaluation workflows across projects
- Improve data validation, artifact management, and clean handoffs between data preparation, training, and downstream delivery
MLOps & Infrastructure
- Build and maintain CI/CD workflows for ML code, including automated testing and containerization
- Support ML development on AWS/Databricks, primarily SageMaker (training & pipelines), S3, and AI/ML experiment tooling
- Improve reproducibility through standardized experiment tracking, configuration management, and lineage tracking
Evaluation & Model Quality
- Build standardized evaluation and validation harnesses — offline quality gates that every model passes before handoff
- Make model quality measurable, comparable, and regression-tested across iterations
- Define and own model performance and drift monitoring in close collaboration with the deployment team
Cross-team Engineering & Standards
- Define shared ML engineering standards and reusable components used across projects
- Collaborate with ML algorithm engineers, data engineers, and the deployment team to negotiate clean interfaces and improve operational delivery
- Document workflows, assumptions, data/serving contracts, and operational procedures
Requirements
Qualifications & Experience:
- 3+ years in either machine learning engineering or software engineering and MLOps
- Strong Python, with a track record of building maintainable and testable ML / data pipeline code
- Hands-on experience with AWS services such as SageMaker, S3, or equivalent cloud ML infrastructure
- Experience with Docker and CI/CD workflows for ML or data applications
- Familiarity with experiment tracking, model versioning, and reproducible ML workflows
- Understanding of model training, evaluation, and validation workflows
- Strong communication and collaboration skills
