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.

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