- Model Development & Implementation : Design, develop, train, validate, and fine-tune machine learning and deep learning models and algorithms to solve complex business problems.

- Data Pipelines & Engineering : Build, maintain, and optimise scalable data pipelines and ETL/ELT workflows for structured and unstructured data sources, ensuring data quality and availability for model training.

- Deployment & MLOps : Deploy ML models into production environments using MLOps (Machine Learning Operations) practices, ensuring reliability, efficiency, and scalability (e.g., using Docker and Kubernetes).

- Monitoring & Optimisation : Monitor model performance in production, detect data or concept drift, implement retraining strategies, and optimize model inference for speed and cost-efficiency.

- Collaboration & Communication : Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to translate business requirements into technical solutions and communicate findings to non-technical stakeholders.

- Research & Innovation : Stay updated with the latest advancements in AI/ML, conduct experiments, and integrate state-of-the-art techniques (e.g., in NLP, Computer Vision, or Generative AI) into production workflows.

Required Skills & Qualifications :

Technical Skills :

- Programming : Strong proficiency in Python is essential, with experience in production-grade systems; familiarity with other languages like Java or Scala is a plus.

- ML Frameworks : Hands-on experience with major ML libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, Keras, or XGBoost.

- Data Handling : Expertise in SQL for data querying and experience with big data technologies/frameworks like Spark, Hadoop, or Kafka.

- Cloud Platforms : Working knowledge of cloud services (AWS, Azure, or GCP) for deploying and scaling ML solutions.

- MLOps & DevOps : Familiarity with tools for version control (Git), containerisation (Docker), orchestration (Kubernetes), and experiment tracking (MLflow).

Domain Knowledge & Soft Skills :

- Strong foundation in applied mathematics, probability, and statistics.

- Excellent problem-solving, analytical, and critical-thinking abilities.

- Strong communication skills, with the ability to explain complex technical concepts to diverse audiences.

- Business acumen to align AI/ML solutions with organisational goals and drive value.

Preferred Qualifications (Plus Points) :

- Master's degree in a relevant AI/ML field.

- Experience with specific domains like Natural Language Processing (NLP) or Computer Vision.

- Experience with Generative AI and large language models (LLMs).