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.