AI Engineer

Job Title

AI Engineer / Machine Learning Engineer

Overview

We are looking for an AI Engineer to design, build, and deploy production-grade machine learning and AI systems. This role focuses on turning models into scalable, reliable services by working across data pipelines, model training, inference, and cloud infrastructure. You will work closely with data engineers and product teams to deliver AI capabilities that have direct business impact.

Responsibilities

Model Development & AI Systems

• Design, train, and evaluate machine learning and AI models for real-world use cases
• Implement feature engineering and model pipelines in collaboration with data engineers
• Apply modern techniques including deep learning, NLP, time-series, or generative models as needed
• Balance model performance, interpretability, latency, and cost

Production & Deployment

• Deploy models into production environments as APIs, batch jobs, or streaming services
• Build scalable inference systems with monitoring for performance and drift
• Optimize models for latency, throughput, and cloud cost
• Implement fallback, versioning, and rollback strategies

MLOps & Infrastructure

• Build and maintain MLOps pipelines for training, evaluation, and deployment
• Implement experiment tracking, model registry, and reproducibility workflows
• Integrate CI/CD pipelines for model and service deployments
• Collaborate on infrastructure design using containerization and cloud services

Data & Collaboration

• Work closely with Data Engineers to define data requirements and feature pipelines
• Partner with product and business stakeholders to translate requirements into AI solutions
• Validate models using real-world data and feedback loops

Monitoring & Improvement

• Monitor model performance, data drift, and system health in production
• Continuously improve models based on new data and changing requirements
• Establish best practices for testing, documentation, and responsible AI usage

Required Qualifications

• 3+ years of experience building machine learning or AI systems
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, or similar)
• Experience deploying models to production environments
• Solid understanding of ML fundamentals (modeling, evaluation, bias, overfitting)
• Experience with cloud platforms (AWS, GCP, or Azure)

Preferred / Nice to Have

• Experience with LLMs, NLP, or generative AI systems
• Familiarity with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI)
• Experience building APIs (FastAPI, Flask, gRPC) for inference
• Knowledge of distributed systems and scalable architectures
• Experience working with time-series or alternative data

What Success Looks Like

• AI models reliably running in production and delivering measurable value
• Clean, repeatable training and deployment pipelines
• Low-latency, cost-efficient inference systems
• Clear ownership of models throughout their lifecycle