AI / Machine Learning Engineer

Role overview An applied AI and machine learning role building intelligent features and products for client engagements across multiple industries. The work covers the full ML lifecycle: designing, training, evaluating, and deploying models, with a strong emphasis on natural language processing, classification, recommendation systems, and applications powered by large language models.

Responsibilities - Design, train, and evaluate machine learning models spanning NLP, classification, and recommendation use cases. - Build LLM-powered applications and agentic workflows, integrating frameworks such as LangChain. - Develop and maintain data pipelines that feed training, evaluation, and inference systems. - Package trained models as production APIs and integrate them into client products. - Apply retrieval-augmented generation (RAG) patterns and vector databases where appropriate. - Collaborate with product and engineering teams to translate business problems into ML solutions.

Requirements - Strong Python skills with hands-on experience in PyTorch or TensorFlow. - Familiarity with LLM application frameworks such as LangChain. - Experience designing data pipelines and evaluating model performance. - Comfort deploying models as APIs and integrating them with back-end services. - Understanding of core ML concepts across supervised learning, NLP, and recommendation systems.

Nice to have - Hands-on experience designing RAG architectures. - Working knowledge of vector databases and embedding stores. - Exposure to LLM orchestration patterns and prompt engineering at scale.