Software Engineer AI/ML

AI EngineerThe CES Business Intelligence team is building the next generation of AI-powered solutions for commercial, contracts, and operations. We're looking for an AI Engineer to help transform GE Aerospace operational data into production-grade machine learning pipelines, models, and LLM-powered applications. This is a multi-faceted engineering role. You'll spend most of your time developing AI/ML products by training models, developing applications, and creating APIs. You will partner closely with analytics teams to enable AI within our existing operational tools. You'll also contribute to AI strategy and partner with executive stakeholders to align on requirements, success metrics, and business impact. We're looking for someone who's excited to expand their technical skillset in AI/ML and deliver advanced solutions that directly impact daily operations.What you'll do:Design, build, deliver, and maintain AI/ML products including LLM-powered applications, forecasting models, anomaly detection systems, and intelligent agents. Own the full AI/ML lifecycle: requirements analysis, model design, training, evaluation, API development, deployment, and operational support. Convert complex operational datasets into scalable AI capabilities that enable real-time decision support.Roles and Responsibilities:AI/ML Product DevelopmentDefine, 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 enterprise.Package AI/ML models as robust, well-documented APIs that enable seamless integration into dashboards, applications, and operational workflows.Collaborate with BI team to embed AI features into existing applications that enable natural language queries, predictive insights, and intelligent recommendations directly within user-facing applicationsTechnical Leadership & CollaborationProvide 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 architectureAI/ML Infrastructure & MLOpsEstablish 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 policiesInnovation & StrategyDrive 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 demonstrationsRequired QualificationsBachelor'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 productionDesired CharacteristicsTechnical ExpertiseWrite 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 monitoring; strong DevOps collaboration skillsExpertise in development platforms and services: AWS, Visual Studio, Databricks, GitHub, etc.Experience building REST APIs (FastAPI, Flask) for model serving; understanding of authentication, rate limiting, versioning, and API documentationDomain & Business AcumenExperience building AI/ML solutions for supply chain, manufacturing, maintenance, or operations analytics is a strong plusUnderstands business metrics and can translate AI/ML capabilities into quantifiable business outcomes (cost savings, time reduction, forecast accuracy improvement)Skilled in breaking down ambiguous AI problems, writing clear problem statements, and estimating model development effort accuratelyStays current on AI/ML industry trends (LLM advancements, new frameworks, emerging techniques); brings practical innovations backed by proof-of-conceptsLeadership & CollaborationLeads by example through delivering AI/ML products while mentoring team on AI integration, prompt engineering, and model usageAble to work through ambiguity and drive alignment between AI capabilities and business needs; communicates model limitations, confidence intervals, and uncertainty clearly to non-technical stakeholdersContinuously measures solutions against user expectations while balancing competing priorities and maintaining build quality.Personal AttributesStrong written and verbal communication skills with the ability to explain complex AI/ML concepts simply and translate effectively between data scientists, software engineers, and business stakeholdersEffective collaborator who works seamlessly with BI developers, platform engineers, and business stakeholdersBusiness-minded approach that focuses on operational metrics, user needs, and business impact while designing AI solutions that solve real problems rather than technical exercisesPersists to completion by driving AI/ML products through deployment, monitoring, and iteration while taking ownership of model performance and continuously improving accuracy