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Urgent! Machine Learning Engineer Job Opening In Erode – Now Hiring BayOne Solutions

Machine Learning Engineer



Job description

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


Required Skill Profession

Prb



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