Senior Machine Learning Engineer

We are looking for experienced Senior Machine Learning Engineers and ML Architects to design, build, and deploy scalable AI/ML solutions that solve complex business problems. In this role, you will work on enterprise-grade data platforms, develop production-ready machine learning and Generative AI applications, and help shape the future of AI-driven products. If you're passionate about Machine Learning, MLOps, Large Language Models (LLMs), and building intelligent systems at scale, we'd love to hear from you.

Responsibilities:

• Design, develop, and deploy production-grade machine learning solutions across diverse business domains.
• Build and optimise enterprise AI applications using Large Language Models (LLMs) and Generative AI.
• Develop Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge repositories.
• Build natural language interfaces for querying structured and unstructured enterprise data.
• Apply MLOps best practices to automate model deployment, monitoring, versioning, and lifecycle management.
• Collaborate with cross-functional teams to deliver scalable, high-performance AI solutions.
• Provide technical guidance on AI/ML architecture, tooling, and best practices.
• Ensure reliability, scalability, and performance of ML models in production environments.

Requirements:

• Senior ML Engineer: 4+ years of relevant industry experience.
• ML Architect: 6+ years of relevant industry experience.
• Strong proficiency in Python and the AI/ML ecosystem.
• Hands-on experience with Pandas, Scikit-learn, MLflow, Gensim, NLTK, TensorFlow, or PyTorch.
• Experience building and deploying production-grade ML solutions on AWS, Azure, or Google Cloud Platform.
• Practical experience with MLOps, including model deployment, monitoring, and drift detection.
• Strong understanding of Machine Learning algorithms, data science workflows, and model evaluation techniques.
• Experience developing LLM-based applications, including Prompt Engineering, RAG, embeddings, and enterprise AI use cases.
• Excellent problem-solving, communication, and collaboration skills.

Preferred Qualifications:

• Experience with Apache Spark for large-scale distributed data processing.
• Hands-on experience with Databricks.
• Experience building enterprise AI applications using LLMs and Generative AI frameworks.
• Master's degree or higher in Computer Science, Engineering, Statistics, Mathematics, Operations Research, or a related quantitative field (or equivalent practical experience).