Ai Ml Engineer

Role Description

We are looking for a highly skilled AI/ML Engineer with 4-6 years of experience in designing, developing, and deploying machine learning and AI solutions. The ideal candidate should have strong hands-on experience in Python, Machine Learning, Deep Learning, Generative AI/LLMs, and building production-ready AI/ML applications.

The candidate will collaborate with data scientists, software engineers, product teams, and business stakeholders to develop scalable AI solutions and integrate machine learning models into enterprise applications.

Role & responsibilities

• Design, develop, train, and deploy Machine Learning and AI models for real-world business problems.
• Develop solutions using Generative AI and Large Language Models (LLMs).
• Build LLM-based applications using techniques such as Prompt Engineering and RAG (Retrieval-Augmented Generation).
• Develop and optimize Machine Learning and Deep Learning models.
• Perform data preprocessing, feature engineering, model training, validation, and performance evaluation.
• Build scalable Python-based AI/ML applications and APIs.
• Integrate ML/AI models with existing applications and enterprise systems.
• Work with vector databases, embeddings, and semantic search for GenAI applications.
• Deploy and monitor ML models in production environments.
• Collaborate with engineering, product, and business teams to translate requirements into AI/ML solutions.
• Follow best practices related to model performance, scalability, security, and maintainability.
• Stay updated with developments in Generative AI, LLMs, Machine Learning, and Deep Learning.

Preferred candidate profile

• 4-6 years of relevant software engineering / AI / ML experience.
• Strong programming experience in Python.
• Strong knowledge of Machine Learning concepts and algorithms.
• Hands-on experience with Deep Learning.
• Experience with ML/DL frameworks such as PyTorch or TensorFlow.
• Hands-on experience with Generative AI and Large Language Models (LLMs).
• Experience developing RAG-based applications.
• Good understanding of Prompt Engineering, Embeddings and Vector Search/Vector Databases.
• Experience working with LLM frameworks such as LangChain, LlamaIndex, or equivalent.
• Strong understanding of the end-to-end ML lifecycle, including data preparation, model development, evaluation, deployment, and monitoring.
• Experience developing/integrating REST APIs for AI/ML solutions.
• Good knowledge of Git and software development practices.
• Strong problem-solving and communication skills.