Artificial Intelligence (AI) Engineer

We are seeking an innovative Artificial Intelligence (AI) Engineer to join our technology team. In this role, you will design, develop, and deploy machine learning and deep learning models to build intelligent systems and features. You will work at the intersection of data science and software engineering, optimizing complex AI algorithms and integrating them seamlessly into production applications.

Key Responsibilities

• Model Design & Development: Research, build, and train machine learning, deep learning, and generative AI models to solve core business problems.
• AI Architecture & MLOps: Design and maintain scalable infrastructure for training, evaluating, deploying, and monitoring AI models in production environments.
• Data Engineering Collaboration: Partner with data engineers to build robust data pipelines, feature stores, and preprocessing workflows for model training.
• API & System Integration: Expose model functionality via RESTful APIs and integrate AI solutions with existing backend systems and microservices.
• Performance Optimization: Optimize algorithms and inference latency to ensure high availability, speed, and cost-efficient cloud resource usage.
• AI Governance & Safety: Ensure AI models adhere to ethical standards, safety protocols, data privacy regulations, and fairness guidelines.

Required Qualifications & Skills

• Experience: 3+ years of professional software engineering experience with a primary focus on AI, Machine Learning, or Deep Learning.
• Programming Skills: Advanced proficiency in Python and relevant deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX).
• AI/ML Concepts: Deep understanding of neural network architectures (Transformers, CNNs, RNNs), reinforcement learning, NLP, or computer vision.
• Cloud & MLOps: Hands-on experience deploying models using cloud platforms (AWS, GCP, or Azure) and tools like MLflow, Kubeflow, or Docker.
• Data Management: Proficiency in SQL and experience handling large structured/unstructured datasets.
• Problem-Solving: Strong quantitative skills, algorithmic thinking, and debugging capabilities.

Preferred Qualifications

• Master’s or Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field.
• Experience fine-tuning Large Language Models (LLMs), RAG (Retrieval-Augmented Generation) architectures, or vector databases (e.g., Pinecone, Milvus, Chroma).
• Contributions to open-source AI projects or publications in recognized conferences (e.g., NeurIPS, ICML, CVPR).

Benefits

• Health, Dental, and Vision Insurance
• Paid Time Off (PTO) & Paid Holidays
• 401(k) / Retirement plan with company match
• Flexible work arrangements (Remote/Hybrid options)
• Annual budget for continuous learning, certifications, and conferences