Senior Machine Learning Engineer

Senior Machine Learning Engineer 

Overview: 

As a Senior Machine Learning Engineer, you will be a Tech Lead of an area within the Core machine Learning team. You will take on a critical leadership role in designing, implementing, and deploying cutting-edge machine learning models that solve real-world business challenges. You will collaborate with various business units to build scalable, high-performance ML systems with a strong emphasis on systems design. In addition to technical contributions, you will mentor junior engineers, drive the full lifecycle of machine learning model development, and ensure that models are seamlessly integrated into production. This position requires expertise in both machine learning and software engineering to develop robust, production-grade solutions. 

Key Responsibilities: 

• Lead the Design & Implementation of ML Models: Lead the architecture and implementation of machine learning models that solve complex business problems, working closely with Product, Business Units, and Data Science teams. 

• Build & maintain Scalable Infrastructure: Design and develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines, ensuring that systems are reliable and performant at scale. 

• Write Production-Grade Code for ML Services and APIs: Write high-quality, maintainable production-grade code that turns machine learning models into deployable services and APIs. Ensure that code is modular and reusable for future ML projects. 

• Optimize Model Performance and Resolve Issues: Debug and troubleshoot model performance issues, track key metrics, and continuously enhance model reliability, speed, and efficiency in production environments. 

• End-to-End Model Lifecycle Management: Own the complete lifecycle of ML models, including monitoring, retraining, and managing versions of models to ensure they continue to meet business needs over time. 

• Leadership and Mentorship: Guide and mentor junior machine learning engineers, promote best practices in software engineering, model development, and deployment. Lead technical decision-making processes and foster collaboration within the team. 

Minimum Qualifications: 

• B.Sc. in Computer Science, Machine Learning, Engineering, or a related technical field. 

• At least 4 years of hands-on experience applying machine learning techniques, including deep learning, reinforcement learning, and NLP in production environments. 

• At least 4 years of experience with SQL, Spark, and scripting languages such as Python for data processing and model development. 

• At least 4 years of experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn for model development. 

• At least 4 years of experience with cloud platforms (AWS, Azure, GCP) and containerization technologies such as Docker, as well as orchestration tools like Kubernetes. 

• Proven experience in deploying machine learning models in a production environment, ensuring scalability, reliability, and high availability. 

• Software Engineering Best Practices: At least 4 years of experience with object-oriented design (OOD), design patterns, and writing clean, maintainable code. Proficiency in version control (Git) and familiarity with Agile methodologies. 

• Distributed Systems Design: At least 4 years of experience & solid understanding of distributed systems and the challenges associated with scaling machine learning models in production, such as managing distributed data processing and microservices architectures. 

• MLOps & Continuous Deployment: Expertise in & at least 4 years of experience in implementing MLOps practices, including setting up continuous integration (CI), continuous delivery (CD), automated testing, and deployment pipelines for machine learning models. 

• Systems Design: At least 4 years of experience & strong understanding of system architecture, performance optimization, and the ability to design fault-tolerant systems that handle large-scale data and high-volume requests. 

• Cloud Engineering: At least 4 years of experience designing and deploying machine learning models using cloud-based environments like AWS, Azure, or Google Cloud. Familiarity with cloud-native tools such as AWS SageMaker, GCP AI Platform, or Azure Machine Learning. 

• Data Pipelines & Streaming:  At least 4 years of experience designing, building, and maintaining ETL pipelines, streamlining data collection, transformation, and storage for model development. Familiarity with tools like Apache Kafka, Apache Flink, and Spark Streaming. 

• Containerization & Orchestration: At least 4 years of experience and proficient in containerizing applications using Docker and managing deployment and scaling using Kubernetes or similar orchestrators. 

• Performance Monitoring & Logging: At least 4 years of experience setting up monitoring and logging systems for tracking model performance in production environments and ensuring efficient resource utilization. 

Preferred Qualifications: 

• Experience with designing and building high-performance distributed systems that handle large-scale data ingestion and processing for machine learning workloads. 

• Experience with real-time inference pipelines and low-latency model serving. 

• Familiarity with serverless computing or managed services for machine learning model deployment. 

• Experience in working with GPU/TPU optimization for accelerated model training and inference. 

• Advanced degree (M.Sc., Ph.D.) in a related field is a plus.