Machine Learning Engineer

As a Senior Analyst – AI & Data Science , you will design, develop, and deliver AI- and data-driven solutions that help our clients achieve measurable business outcomes. This role combines strong Data Science foundations with hands-on AI engineering , including recent GenAI use cases.

You will work across the full data science lifecycle: data exploration, feature engineering, model development, evaluation, and deployment , while also contributing to modern AI solutions such as LLM-based applications, NLP, computer vision, and predictive analytics , primarily on Microsoft Azure .

*Key Role Responsibilities

Day-to-day you will:**

• Design and deliver end-to-end Data Science and AI solutions , from business understanding and data exploration to model deployment and monitoring.
• Perform exploratory data analysis (EDA) , feature engineering, and data preprocessing on structured and unstructured datasets.
• Develop, train, evaluate, and optimize machine learning and deep learning models , selecting appropriate algorithms and validation strategies.
• Contribute to Generative AI solutions , including LLM-based applications, prompt engineering, RAG architectures, and applied NLP use cases.
• Translate business problems into analytical and ML formulations, clearly explaining trade-offs and results to both technical and non-technical stakeholders.
• Support the preparation of client presentations, demos, and proposals , articulating analytical insights and AI-driven value.
• Stay up to date with the latest advancements in Data Science, ML, DL, and GenAI , and actively share knowledge within the team.
• Contribute to reusable assets such as code templates, analytical frameworks, and internal training materials .
• Collaborate with senior team members and architects to identify opportunities where advanced analytics and AI can transform client operations.

Qualification

*Key Role Skill & Capability Requirements

Core Skills**

• Strong foundation in Data Science and applied Machine Learning , including supervised and unsupervised learning.
• Hands-on experience with ML/DL frameworks (e.g., scikit-learn, PyTorch, TensorFlow or equivalent).
• Solid understanding of model evaluation, validation, and performance metrics .
• Experience working with structured and unstructured data , including text data for NLP use cases.
• Proficiency in Python for data analysis and ML development.

• An accelerated and structured training program on Microsoft Azure and AI services .
• Hands-on exposure to real client projects across computer vision, NLP, forecasting, and GenAI (Azure OpenAI, chatbots, RAG) .
• Continuous learning through certifications, mentoring, and internal communities of practice.