[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 optimization Create 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 applications Provide hands-on AI/ML technical leadership for our modernization initiative, setting best practices for prompt engineering, model evaluation, experiment tracking, and responsible AI development Partner with executive stakeholders and BI leadership to understand business challenges and translate operational needs into AI/ML capabilities Ensure AI/ML models deploy reliably to AWS infrastructure with proper monitoring, logging, and performance optimization Translate requirements into a prioritized backlog of AI/ML products, driving delivery to required timelines, quality standards, and measurable business outcomes Collaborate 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 architecture Establish MLOps practices including experiment tracking (MLflow, Weights & Biases), model versioning, automated evaluation pipelines, and A/B testing frameworks for continuous model improvement Drive world-class quality through rigorous SDLC practices Lean/Agile/XP, CI/CD, automated testing, secure coding, scalability patterns, documentation-as-code, refactoring, and performance engineering Implement monitoring and observability for AI/ML systems to track model performance, data drift, prediction latency, and error rates; build automated alerting for model degradation Design vector database architectures and semantic search capabilities to power RAG applications; optimize retrieval strategies for accuracy and latency Build evaluation frameworks for LLM applications—measuring response quality, accuracy, relevance, and hallucination rates; establish automated testing for prompt templates and model outputs Ensure responsible AI practices including bias detection, explainability (SHAP, LIME), privacy-preserving techniques, and compliance with enterprise AI governance policies Drive 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 value Stay current on LLM advancements, ML frameworks, vector databases, and AI application patterns; bring practical innovations that improve decision speed and operational outcomes Engage domain experts to ensure successful transfer of complex operational knowledge into AI models and intelligent systems Establish reusable AI/ML components, templates, and reference architectures that accelerate future development and enable the BI team to leverage AI capabilities independently Communicate 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 university Minimum of 3 years of hands-on AI/ML engineering experience building and deploying machine learning models and/or AI-powered applications to production Write 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 databases Strong foundation in supervised/unsupervised learning, time-series forecasting, classification, and optimization Experience with MLflow, model registries, automated training pipelines, A/B testing frameworks, and model monitoring; strong DevOps collaboration skills Expertise 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,