AI / ML Engineer

Job Description

Job Description :

We are seeking a highly motivated and experienced AL / ML 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 AI / 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

• Service recognition awards
• Market-leading salary packages
• Maternity & Paternity Benefits
• Medical Insurance Benefits

Requirements

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 models.