AI Engineer III

The AI Engineer is responsible for designing, developing, deploying, and maintaining artificial intelligence and machine learning solutions that support intelligent automation, predictive insight, and advanced analytics across the enterprise. As a hands-on builder, this role applies software engineering principles to write production-quality code, build scalable AI systems, including AI Agents and data pipelines, and integrate AI models into new and existing business applications. The AI Engineer collaborates closely with Data Scientists, Data Engineers, ML Ops Engineers, and Platform teams to bring machine learning models from prototype to production. A critical part of this function is to ensure that AI use cases are transitioned from experimentation into reliable, governed, and business-ready solutions by owning their complete operational readiness. This includes implementing robust observability, defining Service Level Objectives (SLOs), and establishing clear incident response and rollback strategies for all AI services. Job Responsibilities: Model Development & Implementation Write clean, efficient, and well-documented code to develop and implement machine learning and AI models that support various business use cases.Implement data engineering and preprocessing workflows required for model inputs.Continuously optimize the performance and scalability of AI applications and models.ML Pipelines & Operations (MLOps) Design, develop, and maintain scalable ML pipelines for model training, validation, inference, and deployment.Collaborate with ML Ops Engineers to package and deploy models into enterprise systems using established MLOps practices.Monitor deployed models in production for performance, data drift, and reliability, and troubleshoot and resolve any issues that arise.Establish and own the operational readiness of all AI services by defining and implementing Service Level Objectives (SLOs) for key metrics, such as p50/p95 latency and availability, and creating robust monitoring and alerting for model drift, latency, and error rates.Collaboration & Integration Work closely with Data Scientists to transition experimental models and research prototypes into robust, production-ready systems.Support the integration of AI capabilities into enterprise workflows, applications, and digital platforms.Contribute to the documentation and explainability of model outputs to ensure clarity for business stakeholders.Governance & Strategy Ensure all deployed AI systems comply with enterprise governance, fairness, and security standards.Evaluate emerging AI technologies, such as LLMs and generative AI, to assess their applicability to business problems and drive innovation. Ensure AI solutions support auditability, explainability, traceability, and regulatory compliance requirementsImplement memory management, context engineering, planning, and multi-step reasoning strategiesDefine and track quality metrics such as groundedness, faithfulness, relevance, task completion rate, and user satisfaction Preferred Skills/Knowledge/Experience: Core Technical & AI Proficiency Strong coding skills in Python, Java, or C++, including API development and software designDeep understanding of core machine learning concepts, including classification, regression, clustering, and deep learning architectures.Hands-on experience with modern deep learning frameworks and algorithms (supervised/unsupervised), such as PyTorch, TensorFlow, or similar for building and training complex neural networks.Skills in working with LLMs, prompt engineering, fine-tuning, and using frameworks like LangChain and LangGraph to build RAG (Retrieval-Augmented Generation) systems.Handling data wrangling, SQL, data warehousing, and ETL pipelines to prepare data for modelsEnd-to-End ML Model Lifecycle Proven experience in the end-to-end model lifecycle: developing, training, and deploying machine learning models from prototype to production.Mastery of data preprocessing, feature engineering, and model evaluation techniques to ensure robust and accurate model performance.Demonstrated ability to build and optimize scalable data pipelines for training and evaluating machine learning models.Strong knowledge of both SQL and NoSQL databases for querying and managing data for AI applications.Software & MLOps Engineering Solid foundation in software engineering best practices, including version control (Git), automated testing, and CI/CD pipelines.Hands-on experience with containerization using Docker and container orchestration with Kubernetes for scalable deployment.Expertise in MLOps observability, including model monitoring to track performance and drift, and establishing model/version lineage, telemetry, and traceability.Experience implementing advanced testing and deployment strategies, including canary/shadow deployments and comprehensive test suites (unit, integration, adversarial, regression).Demonstrated ability to integrate AI models and