AI Engineer

Key Responsibilities

• Design, develop, and operationalize machine learning and generative AI solutions for production environments
• Select, fine-tune, and integrate machine learning and foundation models into scalable systems
• Manage the end-to-end machine learning lifecycle, including data preparation, experimentation, deployment, monitoring, and optimization
• Build robust evaluation frameworks to ensure model quality, accuracy, and performance
• Develop and implement solutions using NLP, Large Language Models (LLMs), Agentic AI, and computer vision technologies where applicable
• Build scalable data pipelines, feature engineering workflows, and model serving infrastructure
• Optimize performance, cost, and scalability across cloud and distributed compute environments
• Collaborate with engineering, product, and cross-functional teams to deliver AI-powered solutions
• Support model governance, observability, testing, and production reliability
• Contribute to continuous improvement of AI systems and deployment frameworks

Required Qualifications

• Bachelor's or Master's degree in Artificial Intelligence, Computer Science, Mathematics, or a related field
• Strong programming expertise in Python
• Hands-on experience with modern machine learning frameworks such as PyTorch and TensorFlow
• Proven experience deploying machine learning models into production environments
• Strong understanding of MLOps concepts and model operationalization
• Experience with Agentic AI systems, LLM-based applications, and generative AI solutions
• Strong understanding of data pipelines, model evaluation, and scalable AI architectures
• Experience working in hardware or infrastructure-focused environments is highly preferred

Preferred Skills

• Background from Amazon, Tesla, Meta, or other top-tier product engineering organizations preferred
• Experience with vector databases, RAG architectures, inference optimization, and model serving frameworks
• Knowledge of cloud platforms such as AWS, GCP, or Azure
• Experience with Kubernetes, Docker, and distributed systems is a plus
• Strong problem-solving, system design, and communication skills

Core Competencies

• Machine Learning Development
• Applied AI and Generative AI
• MLOps and Model Operationalization
• Agentic AI and LLM Systems
• Data and Model Quality
• Experimentation and Evaluation
• Software Engineering
• Technical Communication