Machine Learning Engineer

About The Role

The role drives the development and scaling of core machine learning pipelines, bridging the gap between experimental prototype models and highly performant, production-grade serving infrastructure. The team focuses on deploying robust deep learning and NLP architectures that process high-throughput, low-latency API requests.

You will collaborate closely with data engineers, infrastructure teams, and product managers to integrate machine learning models into real-world applications. The technical environment prioritizes modular software design, scalable feature stores, and automated continuous-integration workflows for machine learning (CD4ML).

Key Responsibilities

• Design, train, and deploy deep learning and transformer-based models for high-throughput NLP and classification tasks in production environments
• Build and optimize scalable data and feature engineering pipelines using Python, PySpark, and modern feature stores
• Implement containerized model serving workflows using Docker, Kubernetes, and Triton Inference Server or FastAPI
• Establish automated MLOps pipelines using MLflow, Kubeflow, or AWS SageMaker for model versioning, tracking, and automated deployment
• Develop real-time monitoring and alerting systems to detect data drift, concept drift, and performance degradation in production models
• Write modular, robust, and well-tested Python code adhering to strict software engineering standards and participating in rigorous peer code reviews

What We Are Looking For

• 3–6 years of professional experience as a Machine Learning Engineer or Software Engineer working with productionized ML systems
• Strong programming proficiency in Python and solid experience with deep learning frameworks such as PyTorch or TensorFlow
• Hands-on experience with cloud infrastructure, specifically managing ML resources within AWS, GCP, or Azure
• Solid understanding of classical machine learning algorithms, deep learning, optimization techniques, and evaluation metrics
• Familiarity with containerization and orchestration technologies, specifically Docker and Kubernetes
• Bonus: Experience with large language model (LLM) fine-tuning, parameter-efficient tuning (LoRA/QLoRA), and vector database integration (Pinecone, Milvus, pgvector)