Senior Machine Learning Engineer

We are seeking a skilled and innovative Machine Learning Engineer with expertise in Large Language Models (LLMs) to join our team. The ideal candidate has hands-on experience developing, fine-tuning, and deploying LLMs, alongside a deep understanding of the machine learning lifecycle. This role involves building scalable AI solutions, collaborating with cross-functional teams, and contributing to cutting-edge AI initiatives.

• Model Development & Optimization:
• oDevelop, fine-tune, and deploy LLMs like OpenAIs GPT, Anthropics Claude, Googles Gemini, or AWS Bedrock.
• oCustomize pre-trained models for specific use cases, ensuring high performance and scalability.
• Machine Learning Pipeline Design:
• oBuild and maintain end-to-end ML pipelines, from data preprocessing to model deployment.
• oOptimize training workflows for efficiency and accuracy.
• Integration & Deployment:
• oWork closely with software engineering teams to integrate ML solutions into production environments.
• oEnsure APIs and solutions are scalable and robust.
• Experimentation & Research:
• oExperiment with new architectures, frameworks, and approaches to improve model performance.
• oStay updated with advancements in LLMs and generative AI technologies.
• Collaboration:
• oCollaborate with cross-functional teams, including data scientists, engineers, and product managers, to align ML solutions with business goals.
• oProvide mentorship to junior team members as needed.

• Experience:
• oAt least 5 years of professional experience in machine learning or AI development.
• oProven experience with LLMs and generative AI technologies.
• Technical Skills:
• oProficiency in Python (required) and/or Java (bonus).
• oHands-on experience with APIs and tools like OpenAI, Anthropics Claude, Google Gemini, or AWS Bedrock.
• oFamiliarity with ML frameworks such as TensorFlow, PyTorch, or Hugging Face.
• oStrong understanding of data structures, algorithms, and distributed systems.
• Cloud Expertise:
• oExperience with AWS, GCP, or Azure, including services relevant to ML workloads (e.g., AWS SageMaker, Bedrock).
• Data Engineering:
• oProficiency in handling large-scale datasets and implementing data pipelines.
• oExperience with ETL tools and platforms for efficient data preprocessing.
• Problem Solving:
• oStrong analytical and problem-solving skills, with the ability to debug and resolve issues quickly.

• Experience with multi-modal models and generative AI for images, text, or other modalities.
• Understanding of ML Ops principles and tools (e.g., MLflow, Kubeflow).
• Familiarity with reinforcement learning and its applications in AI.
• Knowledge of distributed training techniques and tools like Horovod or Ray.
• Advanced degree (Masters or Ph.D.) in Computer Science, Machine Learning, or a related field.