Ai Ml Engineer

Job Summary:

Key Responsibilities:

• Design, implement, and maintain scalable machine learning models and pipelines.
• Work closely with data scientists to transition prototypes into production-ready solutions. Optimize models for performance, scalability, and low-latency inference.
• Deploy models using modern MLOps practices (e.g., CI/CD, model monitoring, versioning). Collaborate with data engineers to build efficient data pipelines and feature stores.
• Continuously monitor and improve the performance of deployed models.
• Integrate AI/ML systems with cloud services, APIs, and front-end applications.
• Stay up to date with the latest developments in AI/ML frameworks, tools, and research.

Required Qualifications:

• Bachelors or Masters degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in machine learning, software engineering, or a related area.
• Strong programming skills in Python, R Programming Language and experience with ML libraries such as TensorFlow, PyTorch, or scikit-learn.
• Experience deploying ML models in production (e.g., REST APIs, batch processing, real-time inference).
• Familiarity with cloud platforms like AWS, OCI, GCP, or Azure (e.g., Sagemaker, Vertex AI).
• Solid understanding of software engineering best practices (version control, testing, debugging). Strong knowledge of statistical analysis, predictive modelling, and deep learning.
• Experience working with large datasets and distributed computing tools (e.g., Spark, Dask). Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).

Preferred Qualifications:

• Experience with deep learning, NLP, or computer vision.
• Knowledge of distributed systems and big data tools (e.g., Spark, Kafka, Hadoop).
• Familiarity with MLOps tools (e.g., MLflow, Kubeflow, Airflow).
• Experience with model interpretability and explainability techniques.
• Contributions to open-source AI/ML projects or research publications.