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We are seeking a highly motivated and experienced Machine Learning Engineer to join our team. You will be responsible for transforming Data Science prototypes into production-ready applications, building scalable MLOps pipelines, and maintaining deployed models. This role requires a strong background in software engineering, cloud infrastructure, and practical experience with the full ML lifecycle.
Key Responsibilities:
Production Deployment & Scaling:
Refactor and optimise Data Science prototypes for performance and efficiency.
Containerise models using Docker and deploy them onto a scalable infrastructure, such as Kubernetes.
Design and build low-latency APIs to serve model predictions in real-time.
MLOps Pipeline Automation:
Implement CI/CD/CT pipelines to automate model training, testing, and deployment processes.
Utilise orchestration tools like Airflow or Kubeflow to manage and schedule complex ML workflows.
Advanced AI Focus:
Work with specialised models (e.g., LLMs) on tasks such as fine-tuning and prompt engineering.
Contribute to ensuring the ethical and compliant use of AI systems, particularly in relation to data privacy and bias.
System Monitoring & Maintenance:
Set up robust monitoring for model performance, data drift, and infrastructure health in production.
Develop and manage automated processes for model retraining, versioning, and rollbacks.
Software Engineering & Collaboration:
Write high-quality, clean, modular Python code following professional software engineering standards.
Collaborate closely with Data Scientists and Data Engineers to ensure seamless data flow and feature availability for model
Requirements
B.E./ B.Tech./ M.E./ M.Tech./ MCA/M.Sc.IT or related field
2-4 years of professional experience in ML Engineering, Software Engineering with ML focus, or related role.
Proven experience deploying and managing ML models in a production environment.
High proficiency in Python and experience writing production-grade, object-oriented code.
Practical experience with popular ML/DL libraries like TensorFlow, PyTorch, or Scikit-learn.
Strong knowledge of SQL and experience with big data technologies (Spark/PySpark, Hadoop) for data manipulation and feature extraction.
Experience with essential MLOps tools, including Docker (containerisation) and orchestration tools like Airflow or Kubeflow.
Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) for deploying ML services.
Familiarity with version control best practices (e.g., Git).
Strong analytical and problem-solving skills
Excellent communication and interpersonal skills
Nice to Have Skills
Experience with Kubernetes for model serving and scaling.
Familiarity with other languages like Java or C++ for performance optimisation.
Knowledge of specific ML domains (e.g., NLP, Recommender Systems).
Experience with Agile methodologies
Benefits
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