Senior AI/ML Engineer

Job Summary:

We are looking for a results-driven Senior AI/ML Engineer to lead the development and deployment of scalable machine learning models and intelligent systems. You will be at the forefront of building AI solutions that solve high-value business problems, with full ownership from data preparation to model monitoring. This is a key role in a hands-on, production-grade AI/ML team, working with state-of-the-art tooling and infrastructure.

Key Responsibilities:

• Design and implement robust, end-to-end ML models and AI pipelines to solve real-world business challenges.
• Build and maintain scalable data pipelines for structured and unstructured datasets — including data extraction, cleansing, feature engineering, and labeling.
• Train and tune ML models using modern frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Deploy models to production using MLOps tools like MLflow, Kubeflow, or Amazon SageMaker.
• Collaborate with Product, Engineering, and Data Science teams to embed ML into customer-facing solutions.
• Monitor and optimise models for performance, reliability, and drift detection in live environments.
• Conduct R&D on cutting-edge techniques in LLMs, NLP, and computer vision, and apply them to production use cases.
• Build internal dashboards, logs, and traceability features to ensure robust model governance.
• Write clean, reusable code and maintain thorough documentation of solutions and processes.

Required Skills & Qualifications:

• 8+ years of experience in machine learning, AI engineering, or applied data science roles.
• Deep fluency in Python and core ML libraries: NumPy, Pandas, scikit-learn, TensorFlow, PyTorch.
• Strong knowledge of ML algorithms, statistical modeling, and deep learning architectures (CNNs, RNNs, Transformers).
• Experience with cloud platforms such as AWS, Azure, or GCP for training and deploying models.
• Proficiency with Docker, Kubernetes, and DevOps tools for ML deployment.
• Hands-on experience with MLOps frameworks like MLflow, Kubeflow, or SageMaker.
• Familiarity with CI/CD pipelines, model registries, and version control best practices.
• Ability to work with large-scale, multimodal datasets (text, time series, images, etc.).
• Strong analytical, problem-solving, and collaboration skills.

Preferred Qualifications:

• Master’s degree in computer science, AI, Data Science, or a related field.
• Experience building applications using LLMs (e.g., GPT, BERT) or working on NLP and Computer Vision problems.
• Familiarity with Big Data tools such as Spark, Kafka, Databricks.
• Contributions to open-source ML/AI projects or peer-reviewed publications.
• Awareness of AI ethics, data privacy regulations, and responsible AI deployment practices.