Overview
Responsibilities [About the Role]
We are looking for talented AI/ML engineers to help build, evaluate, and optimize next-generation AI systems. In this role, you will design and improve machine learning pipelines, fine-tune large language models, develop generative AI applications, and contribute to the research and engineering workflows that power real-world AI deployments.
This is a highly technical role ideal for engineers who enjoy solving complex ML problems, working with modern AI architectures, and pushing the boundaries of model performance and capability.
[What You’ll Do]
As an AI/ML & LLM Engineer, you will:
• Build, train, and optimize machine learning models, including LLMs and multimodal models.
• Develop and maintain ML pipelines, including data preprocessing, training, inference, monitoring, and performance optimization.
• Fine-tune LLMs for downstream tasks and design evaluation strategies to measure progress.
• Implement RAG (Retrieval-Augmented Generation) systems using vector databases and orchestration frameworks.
• Collaborate with cross-functional teams to design user-facing AI features and integrate models into production workflows.
• Conduct research explorations, benchmark different architectures, and experiment with new methods from recent papers.
• Build and deploy scalable ML services using Python-based frameworks, cloud platforms, and containerization tools.
• Document engineering decisions, model behavior, experiments, and results clearly.
Qualifications • Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, AI, Engineering, or a related field.
• Strong hands-on experience training, fine-tuning, or deploying ML models (2+ years preferred).
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX.
• Deep understanding of LLM internals: attention mechanisms, transformer architectures, embeddings, tokenization, and evaluation.
• Knowledge of RAG pipelines, vector databases, and prompt engineering.
• Experience with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes).
• Strong analytical, debugging, and problem-solving skills.
• Excellent English communication and documentation skills.
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