AI Engineer

Key Responsibilities

• Design, develop, and deploy production-grade machine learning models and generative AI applications using state-of-the-art frameworks and methodologies
• Build and optimize Retrieval Augmented Generation (RAG) pipelines for enterprise knowledge systems and intelligent document processing
• Implement Model Context Protocol (MCP) and agent-to-agent (A2A) context engineering solutions for complex AI orchestration
• Create feedback loops and monitoring systems to continuously improve model performance and ensure reliability
• Architect and maintain MLOps pipelines for model training, versioning, deployment, and monitoring
• Create AI agents using LangChain and LangGraph frameworks for autonomous decision-making and workflow automation
• Collaborate with data engineering teams to build robust data pipelines and feature engineering workflows
• Mentor junior data scientists and contribute to the development of AI best practices and standards

Required Qualifications

• 5+ years of experience in data science, machine learning, or related field
• Strong expertise in generative AI technologies including LLMs, prompt engineering, and context management
• Hands-on experience building RAG systems with vector databases and semantic search
• Proficiency with LangChain, LangGraph, and agent-based architectures
• Experience with Model Context Protocol (MCP) and A2A context engineering patterns
• Deep understanding of traditional machine learning algorithms (regression, classification, clustering, time series)
• Strong MLOps experience including CI/CD pipelines, model versioning, and monitoring frameworks
• Proficiency with Hugging Face ecosystem (Transformers, Datasets, Hub)
• Experience with TensorFlow and/or PyTorch for model development
• Strong Python programming skills with experience in production-grade code
• Experience designing and implementing feedback loops for continuous model improvement
• Knowledge of cloud platforms (AWS, Azure, or GCP) for ML deployment
• Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders

Preferred Qualifications

• Experience in regulated industries (financial services, healthcare)
• Knowledge of data governance and model risk management frameworks
• Experience with distributed training and large-scale model deployment
• Familiarity with other frameworks like Anthropic's Claude API, OpenAI API
• Experience with vector databases (Pinecone, Weaviate, Chroma)
• Understanding of prompt engineering and fine-tuning techniques
• Contributions to open-source ML/AI projects

Technical Skills

• Languages: Python, SQL
• ML Frameworks: TensorFlow, PyTorch, scikit-learn
• GenAI Tools: LangChain, LangGraph, Hugging Face
• MLOps: Docker, Kubernetes, MLflow, Weights & Biases
• Cloud: AWS/Azure/GCP machine learning services
• Data: Pandas, NumPy, vector databases
• Version Control: Git, CI/CD pipelines

Benefits

Competitive compensation with performance incentive awards, Health Insurance, Dental Insurance, a 401K and DC Plan for your retirement, LTD & Life Insurance, Day Care Reimbursement, Tuition Assistance for graduate and undergraduate programs, an award-winning Wellness program and much more!