Description :

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 :

- Masters 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.