Machine Learning Engineer

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)