ML Engineer

Role Overview
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.
Key Responsibilities
Model Development & Optimization:
Develop, fine-tune, and deploy LLMs like OpenAI's GPT, Anthropic's Claude, Googles Gemini, or AWS Bedrock.
Customize pre-trained models for specific use cases, ensuring high performance and scalability.
Machine Learning Pipeline Design:
Build and maintain end-to-end ML pipelines, from data preprocessing to model deployment.
Optimize training workflows for efficiency and accuracy.
Integration & Deployment:
Work closely with software engineering teams to integrate ML solutions into production environments.
Ensure APIs and solutions are scalable and robust.
Experimentation & Research:
Experiment with new architectures, frameworks, and approaches to improve model performance.
Stay updated with advancements in LLMs and generative AI technologies.
Collaboration:
Collaborate with cross-functional teams, including data scientists, engineers, and product managers, to align ML solutions with business goals.
Provide mentorship to junior team members as needed.
Required Qualifications
Experience:
At least 5 years of professional experience in machine learning or AI development.
Proven experience with LLMs and generative AI technologies.
Technical Skills:
Proficiency in Python (required) and basic knowledge of Java is needed
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.
Strong understanding of data structures, algorithms, and distributed systems.
Cloud Expertise:
Experience with AWS, GCP, or Azure, including services relevant to ML workloads (e.g., AWS SageMaker, Bedrock).
Data Engineering:
Proficiency in handling large-scale datasets and implementing data pipelines.
Experience with ETL tools and platforms for efficient data preprocessing.
Problem Solving:
Strong analytical and problem-solving skills, with the ability to debug and resolve issues quickly.
Preferred Qualifications
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.