Machine Learning Engineer

Responsibilities :

Machine Learning Development & Implementation (40%)

Design and implement end-to-end ML pipelines for recommendation systems, search ranking, and classification problems

Build and optimize traditional ML models using techniques such as ensemble methods, SVMs, gradient boosting, and neural networks

Develop time series forecasting models and ranking algorithms for complex business applications

Implement feature engineering pipelines that handle real-world data noise and edge cases

Create robust data preprocessing and validation systems that ensure model reliability in production

Production ML Systems & Deployment (25%)

Deploy ML models using Docker containerization and REST API frameworks (Flask / FastAPl)

Implement model serving solutions on Azure Container Instances with proper monitoring and

alerting

Build MLOps pipelines using MLflow for experiment tracking and model registry management

Design scalable data workflows using Apache Airflow and Azure Data Factory for ETL operations

Establish model versioning, rollback strategies, and performance monitoring in production environments

Technical Leadership & Collaboration (20%)

Serve as a technical sounding board for AI team members on ML architecture and approach decisions

Mentor team members on best practices for production ML system design and implementation

Communicate complex technical concepts clearly to both technical and non-technical stakeholders

Collaborate across AI, web development, and system architecture teams toensure seamless integration

Guide strategic decisions on when to use traditional ML versus generative AI approaches

Strategic ML Decision Making (15%)

Evaluate problems to determine optimal solutions : classical ML, GenAI, or simpler analytical methods

Integrate generative AI tools effectively into workflows without over-relying on them

Design ML systems that integrate seamlessly with existing web application architectures

Provide technical guidance onmodel selection, evaluation metrics, and performance optimization

Stay current with ML best practices while maintaining focus on practical, business-driven solutions

Required Qualifications

Education

& Experience

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related technical field

4+ years of hands-on experience building and deploying machine learning systems in production

Proven experience working in non-technical business domains (healthcare, finance, retail, HR, etc.)

Track record of mentoring technical team members and leading collaborative projects

Core Technical Skills

Programming Excellence : Expert-level Python proficiency with focus on clean, maintainable, production-ready code

Traditional ML Expertise : Deep understanding of classification, regression, ranking, and recommendation algorithms

Production ML :

Experience with MLOps practices, model deployment, monitoring, and lifecycle management

Data Engineering : Proficiency with data pipeline development, ETL processes, and handling messy real-world datasets

Cloud Platforms : Hands-on experience with Azure ML Studio, Azure Container Instances, and Azure Data Factory

Specialized Experience :

Experience building recommendation engines, search ranking systems, or time series forecasting models

Background in A / B testing methodologies and measuring business impact of ML initiatives

Knowledge of feature stores, model registry systems, and ML experiment tracking

Understanding of model interpretability, bias detection, and fairness in ML systems

Experience with both structured and unstructured data processing at scale

Experience with deep learning frameworks (TensorFlow, PyTorch) for appropriate use cases

Preferred Qualifications

Knowledge of natural language processing techniques and text classification systems

Background in building ML systems for talent acquisition, recruiting, or HR technology

Experience with real-time ML inference and low-latency model serving

Understanding of distributed computing and large-scale data processing