Note: The job is a remote job and is open to candidates in USA. Dice is hiring an AI Engineer III to design, develop, deploy, and maintain artificial intelligence and machine learning solutions supporting intelligent automation, predictive insight, and advanced analytics across the enterprise. The role focuses on building production-quality AI systems and pipelines, integrating models into business applications, and ensuring operational readiness, governance, observability, and compliance.
Responsibilities
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
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
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
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 requirements
Implement memory management, context engineering, planning, and multi-step reasoning strategies
Define and track quality metrics such as groundedness, faithfulness, relevance, task completion rate, and user satisfaction
Skills
Bachelor's degree in Computer Science, Data Science, Engineering, or related field is required
Needs 3-5+ years of dedicated experience designing and shipping ML models to production
Should have led the design of a significant ML-powered feature
Strong coding skills in Python, Java, or C++, including API development and software design
Deep 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 models
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
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 services into enterprise applications by building and consuming RESTful APIs
Proficiency with at least one major cloud platform (Google Cloud Platform, AWS, Azure) and its associated AI/ML services (e.g., Vertex AI, SageMaker, Azure ML)
