Senior Machine Learning Engineer

We are looking for a skilled and innovative Machine Learning Engineer with expertise in Large Language Models (LLMs) to join our team. The ideal candidate should have hands-on experience in developing, fine-tuning, and deploying LLMs, along with a deep understanding of the machine learning lifecycle. Your responsibilities will include developing and optimizing LLMs such as OpenAI's GPT, Anthropic's Claude, Google's Gemini, or AWS Bedrock. You will customize pre-trained models for specific use cases to ensure high performance and scalability. Additionally, you will be responsible for designing and maintaining end-to-end ML pipelines from data preprocessing to model deployment, optimizing training workflows for efficiency and accuracy. Collaboration with cross-functional teams, integration of ML solutions into production environments, experimentation with new approaches to improve model performance, and staying updated with advancements in LLMs and generative AI technologies will also be part of your role. You will collaborate with data scientists, engineers, and product managers to align ML solutions with business goals and provide mentorship to junior team members. The qualifications we are looking for include at least 5 years of professional experience in machine learning or AI development, proven expertise with LLMs and generative AI technologies, proficiency in Python (required) and/or Java (bonus), hands-on experience with APIs and tools like OpenAI, Anthropic's Claude, Google Gemini, or AWS Bedrock, familiarity with ML frameworks such as TensorFlow, PyTorch, or Hugging Face, and a strong understanding of data structures, algorithms, and distributed systems. Cloud expertise in AWS, GCP, or Azure, including services relevant to ML workloads such as AWS SageMaker and Bedrock, proficiency in handling large-scale datasets and implementing data pipelines, experience with ETL tools and platforms for efficient data preprocessing, strong analytical and problem-solving skills, and the ability to debug and resolve issues quickly are also required. Preferred qualifications include experience with multi-modal models, generative AI for images, text, or other modalities, understanding of ML Ops principles and tools like MLflow and Kubeflow, familiarity with reinforcement learning and distributed training techniques and tools like Horovod or Ray, and an advanced degree (Master's or Ph.D) in Computer Science, Machine Learning, or a related field.,