Machine Learning Engineer

Role Summary


We are looking for an AI / ML Engineer to design, build, and evaluate AI capabilities for enterprise systems. The role covers predictive modelling, anomaly detection, classification, recommendation, decision support, workflow assistance, and responsible use of generative AI.

Responsibilities

• 
  Design and implement AI/ML solutions for enterprise use cases.
• Build, evaluate, and optimize models for anomaly detection, forecasting, classification, scoring, recommendation, and decision support.
• Work with structured and unstructured data, including time-series, tabular, text, logs, events, records, transactions, and user feedback.
• Define features, labels, training approach, validation approach, and model performance metrics.
• Perform model testing, tuning, drift monitoring, and performance evaluation.
• Design feedback loops based on user actions, system outcomes, and model performance.
• Explore reinforcement learning where suitable for policy optimization, prioritization, recommendation, or workflow tuning.
• Design and implement LLM/RAG workflows for summarization, explanation, Q&A, knowledge retrieval, and task assistance.
• Implement AI guardrails, grounding, audit trail, confidence display, and human approval workflow where required.
• Apply appropriate AI techniques based on use case, reliability requirements, and governance needs.
• Work with data, application, and platform teams to ensure data quality, traceability, and model input readiness.
• Present AI approach clearly to technical and non-technical stakeholders.

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, Mathematics, Statistics, Engineering, or a related technical field.
• Strong understanding of supervised learning, unsupervised learning, anomaly detection, classification, forecasting, model validation, model drift, and performance evaluation.
• Experience with Python and ML libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, or similar.
• Experience working with time-series, tabular, text, or operational data.
• Understanding of neural networks, statistical modelling, and model selection trade-offs.
• Familiarity with LLMs, RAG, embeddings, retrieval, guardrails, and AI evaluation.
• Strong analytical, problem-solving, and communication skills.