AI Engineer II

Quick Facts

• Role: AI Engineer II

Description

The AI Engineer II designs, develops, deploys, and maintains AI and machine learning solutions for intelligent automation, predictive insights, and advanced analytics across the enterprise. This is a hands-on engineering role building production-quality code, scalable AI systems (including AI Agents and data pipelines), and integrating AI models into enterprise applications. The role owns operational readiness by implementing observability, defining SLOs, and setting incident response and rollback strategies to move AI use cases from experimentation to governed, reliable production.

Role Responsibilities

• Develop and implement machine learning and AI models with clean, well-documented production code

• Build data engineering and preprocessing workflows to prepare model inputs

• Continuously optimize model and AI application performance and scalability

• Design, develop, and maintain scalable ML pipelines for training, validation, inference, and deployment

• Package and deploy models using MLOps practices and collaborate with ML Ops teams

• Monitor deployed models for performance, data drift, and reliability; troubleshoot and resolve production issues

• Define and implement SLOs for AI services (including p50/p95 latency and availability) and set up monitoring and alerting

• Transition prototypes into robust production-ready systems with Data Scientists

• Integrate AI capabilities into enterprise workflows, applications, and digital platforms

• Contribute to documentation and model explainability for business stakeholders

• Ensure AI systems comply with enterprise governance, fairness, and security standards

• Evaluate emerging AI technologies (including LLMs and generative AI) for business applicability

Requirements

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field

• Completed academic projects or internships involving data manipulation (Pandas), building simple models (Scikit-learn), and basic deep learning knowledge (PyTorch/TensorFlow)

• Strong coding skills in Python, Java, or C++ and experience developing production APIs

• Deep understanding of core ML concepts (classification, regression, clustering, deep learning)

• Hands-on deep learning experience with PyTorch or TensorFlow

• LLM experience including prompt engineering and fine-tuning, plus ability to build RAG systems using frameworks such as LangChain and LangGraph

• Experience with data wrangling, SQL, data warehousing, and ETL pipelines

• Proven end-to-end ML lifecycle experience (prototype to production)

• Solid MLOps/software engineering foundation (Git, automated testing, CI/CD)

• Containerization and orchestration experience (Docker, Kubernetes)

• MLOps observability experience (model monitoring, lineage, telemetry, traceability)

• Experience integrating AI models/services into enterprise apps via RESTful APIs

• Cloud proficiency with major providers (GCP, AWS, or Azure) and associated AI/ML services

• Big data processing experience with Apache Spark or similar

• Ability to collaborate in Agile and communicate clearly with technical and non-technical stakeholders

Benefits

Comprehensive benefits package (details available upon request).