The posting
Job Description – add details here
Seeking a Machine Learning Engineer for the following role - Generative AI & MLFrameworks: PyTorch, TensorFlow, Hugging Face Transformers, DiffusersTraining: DeepSpeed, Accelerate, Ray, distributed training frameworksModels: GPT/LLaMA variants, DALL-E/Stable Diffusion, Whisper, multi-modal modelsFine-tuning: LoRA, QLoRA, DreamBooth, custom training pipelinesInfrastructure & PlatformsCloud: GCP Vertex AI, Azure OpenAI, AWS Bedrock, multi-cloud orchestrationServing: TensorRT, ONNX, TorchServe, custom inference serversOrchestration: Kubernetes, Docker, APIGEE, TerraformData: Vector databases (Pinecone, Weaviate), feature stores, data versioningSpecialized ToolsFrameworks: Autogen, LangChain, MCP (Model Context Protocol)Evaluation: Custom metrics, human evaluation platforms, A/B testing frameworksMonitoring: MLflow, Weights & Biases, custom dashboardsResponsibilitiesBuild text-to-image and text-to-video generation systemsDevelop speech synthesis and voice cloning models with safety guardrails for character voicesCreate image-to-text and video-to-text systems for content analysis and accessibilityImplement cross-modal generation (text + image ? video, audio + text ? multimedia content)Build real-time generative systems for interactive experiences (IoT)Model Evaluation & Quality AssuranceDesign and implement custom evaluation models for content assessment (brand safety, content ratings, character consistency)Build automated benchmarking systems for generative model performance across multi-cloud environmentsDevelop specialized ML pipelines for hallucination detection, bias measurement, and factual accuracy assessmentCreate domain-specific evaluation frameworks for use cases (content appropriateness, brand alignment, safety compliance)Implement human-in-the-loop evaluation systems with domain expertsResearch & Advanced TechniquesImplement cutting-edge generative AI techniques: diffusion models, transformer variants, mixture of expertsDevelop constitutional AI and AI safety techniques for responsible content generationBuild adversarial training systems to improve model robustnessResearch and implement prompt engineering and in-context learning optimizationCreate novel architectures for specific generative tasksProduction AI/ML SystemsDesign A/B testing frameworks for generative model comparison and optimizationBuild real-time inference optimization for low-latency content generationImplement model serving infrastructure with auto-scaling and load balancingCreate model monitoring, drift detection, and automatic retraining systemsDevelop caching and retrieval systems for improved generative AI performanceKey Projects & Use Cases (Marketing Content Generation)Build text-to-video systems for promotional content creationDevelop brand-consistent image generation with style transferCreate voice synthesis for character-based marketing campaignsTheme Park InnovationImplement real-time generative systems for interactive guest experiencesBuild personalized content generation based on guest preferencesDevelop safety-aware content generation for operational communicationsCustomer Experience EnhancementCreate personalized response generation for customer supportBuild multi-lingual content generation for global audiencesDevelop accessibility-focused content generation (audio descriptions, simplifiedlanguage) Basic Qualifications5+ years of hands-on machine learning engineering with 2+ years focused on generative AIStrong experience with transformer architectures, diffusion models, and large language modelsProven track record with model fine-tuning, RLHF, and parameter-efficient training techniquesExperience with multi-modal AI systems (text+vision, text+audio, cross-modal generation)Deep understanding of generative AI training dynamics, loss functions, and optimization techniques Technical ExpertiseExpert-level Python programming with TensorFlow/PyTorch and distributed training frameworksExperience with cloud ML platforms (GCP Vertex AI, Azure OpenAI, AWS Bedrock) and model servingStrong background in computer vision, NLP, and audio processing for generative applicationsKnowledge of MLOps, model versioning, and production deployment strategiesExperience with vector databases, embeddings, and retrieval-augmented generation (RAG) AI Safety & EvaluationExperience building evaluation frameworks for generative AI systemsKnowledge of AI safety techniques: bias detection, content filtering, adversarial robustnessUnderstanding of responsible AI frameworks and red teaming methodologiesFamiliarity with AI governance, model interpretability, and compliance requirements Preferred QualificationsAdvanced degree in Machine Learning, Computer Science, or related fieldExperience with industry applications (content creation, media analysis, interactive systems)Knowledge of edge AI optimization and real-time inference systemsBackground in reinforcement learning and human preference modelingExperience with large-scale distributed training (multi-GPU, multi-node)Contributions to open-source AI projects or published research in generative AIEducation BE/BS in Machine Learning, Computer Science, or related field
Skills and Requirements – add details here
Basic Qualifications5+ years of hands-on machine learning engineering with 2+ years focused on generative AIStrong experience with transformer architectures, diffusion models, and large language modelsProven track record with model fine-tuning, RLHF, and parameter-efficient training techniquesExperience with multi-modal AI systems (text+vision, text+audio, cross-modal generation)Deep understanding of generative AI training dynamics, loss functions, and optimization techniques
