[Remote] Software Engineer AI/ML

Note: The job is a remote job and is open to candidates in USA. GE Aerospace is seeking an AI Engineer to help transform operational data into production-grade machine learning pipelines and LLM-powered applications. The role involves developing AI/ML products, collaborating with analytics teams, and contributing to AI strategy while ensuring successful deployment and operational support of AI solutions. Responsibilities Define, build, and evolve AI-powered software products that accelerate Commercial Engine Services operations—including LLM applications, machine learning models, and intelligent automation for supply chain optimizationCreate Model Context Protocol (MCP) servers that package domain-specific AI capabilities for reuse across the enterprisePackage AI/ML models as robust, well-documented APIs that enable seamless integration into dashboards, applications, and operational workflowsCollaborate with BI team to embed AI features into existing applications that enable natural language queries, predictive insights, and intelligent recommendations directly within user-facing applicationsProvide hands-on AI/ML technical leadership for our modernization initiative, setting best practices for prompt engineering, model evaluation, experiment tracking, and responsible AI developmentPartner with executive stakeholders and BI leadership to understand business challenges and translate operational needs into AI/ML capabilitiesEnsure AI/ML models deploy reliably to AWS infrastructure with proper monitoring, logging, and performance optimizationTranslate requirements into a prioritized backlog of AI/ML products, driving delivery to required timelines, quality standards, and measurable business outcomesCollaborate with data platform teams to design data pipelines that feed AI/ML models to ensure data quality, freshness, and proper feature engineering from the Databricks medallion architectureEstablish MLOps practices including experiment tracking (MLflow, Weights & Biases), model versioning, automated evaluation pipelines, and A/B testing frameworks for continuous model improvementDrive world-class quality through rigorous SDLC practices: Lean/Agile/XP, CI/CD, automated testing, secure coding, scalability patterns, documentation-as-code, refactoring, and performance engineeringImplement monitoring and observability for AI/ML systems to track model performance, data drift, prediction latency, and error rates; build automated alerting for model degradationDesign vector database architectures and semantic search capabilities to power RAG applications; optimize retrieval strategies for accuracy and latencyBuild evaluation frameworks for LLM applications—measuring response quality, accuracy, relevance, and hallucination rates; establish automated testing for prompt templates and model outputsEnsure responsible AI practices including bias detection, explainability (SHAP, LIME), privacy-preserving techniques, and compliance with enterprise AI governance policiesDrive the AI/ML roadmap for Commercial Engine Services BI team by identifying high-impact use cases, evaluating emerging AI technologies, and building proof-of-concepts that demonstrate business valueStay current on LLM advancements, ML frameworks, vector databases, and AI application patterns; bring practical innovations that improve decision speed and operational outcomesEngage domain experts to ensure successful transfer of complex operational knowledge into AI models and intelligent systemsEstablish reusable AI/ML components, templates, and reference architectures that accelerate future development and enable the BI team to leverage AI capabilities independentlyCommunicate AI/ML concepts, tradeoffs, and results to non-technical stakeholders through clear documentation, executive presentations, and live demonstrations Skills Bachelor's Degree in Computer Science, Data Science, Statistics, Engineering, or related field from an accredited college or universityMinimum of 3 years of hands-on AI/ML engineering experience building and deploying machine learning models and/or AI-powered applications to productionWrite production-quality code that meets standards and delivers intended functionality using the most appropriate technologies for the project (e.g., Python, Java, C#, TypeScript—based on system needs)Proven experience building data platforms and production LLM-powered applications; strong understanding of prompt engineering, retrieval-augmented generation, and vector databasesStrong foundation in supervised/unsupervised learning, time-series forecasting, classification, and optimizationExperience with MLflow, model registries, automated training pipelines, A/B testing frameworks, and model monitor