Machine Learning/Generative AI Engineer

As a Machine Learning & Generative AI Engineer, you will play a crucial role in designing, building, and deploying advanced ML and GenAI solutions. You will have the opportunity to work on cutting-edge AI technologies like LLM fine-tuning, Transformer architectures, Retrieval-Augmented Generation (RAG), and enterprise-scale ML systems. Your innovative thinking and technical expertise will be instrumental in delivering impactful AI solutions aligned with business needs. **Key Responsibilities:** - Design, build, and deploy end-to-end ML pipelines, including data preprocessing, feature engineering, model training, and deployment. - Develop and optimize LLM-based solutions using Transformer architectures for enterprise applications. - Implement RAG pipelines by leveraging embeddings and vector databases to integrate domain-specific knowledge into LLMs. - Fine-tune LLMs on custom datasets for specialized tasks such as text corpora, Q&A, conversational data, and structured-to-text. - Ensure scalable deployment of ML & LLM models on cloud environments with monitoring, versioning, and performance optimization. - Collaborate with data scientists, domain experts, and software engineers to deliver AI-driven business impact. **Qualifications Required:** - 3 to 7 years of experience in Machine Learning, Deep Learning, and AI model development. - Strong proficiency in Python and ML frameworks like PyTorch, TensorFlow, Scikit-Learn, and MLflow. - Expertise in Transformer architectures (BERT, GPT, T5, LLaMA, Falcon, etc.) and attention mechanisms. - Hands-on experience with Generative AI, including LLM fine-tuning, instruction tuning, and prompt optimization. - Experience with RAG pipelines, embeddings, vector databases, and retrieval workflows. - Strong foundation in statistics, probability, and optimization techniques. - Proficiency with cloud ML platforms such as Azure ML, AWS SageMaker, and GCP Vertex AI. - Familiarity with Big Data & Data Engineering tools like Spark, Hadoop, Databricks, and SQL/NoSQL databases. - Hands-on experience with CI/CD, MLOps, and automation pipelines using tools like Airflow, Kubeflow, and MLflow. - Experience with Docker and Kubernetes for scalable ML/LLM deployment. In addition to technical skills, you are expected to excel in real-world AI application & deployment, prompt engineering & model evaluation, fine-tuning & customization of LLMs, understanding of LLM internals & ecosystem tools, creativity, problem-solving & innovation, and business alignment, security, and ethical AI practices. (Note: Any additional details of the company were not provided in the job description),