Machine Learning Engineer

Seeking a Machine Learning Engineer for the following role - Generative AI & ML Frameworks: PyTorch, TensorFlow, Hugging Face Transformers, Diffusers Training: DeepSpeed, Accelerate, Ray, distributed training frameworks Models: GPT/LLaMA variants, DALL‑E/Stable Diffusion, Whisper, multi‑modal models Fine‑tuning: LoRA, QLoRA, DreamBooth, custom training pipelines Infrastructure & Platforms Cloud: GCP Vertex AI, Azure OpenAI, AWS Bedrock, multi‑cloud orchestration Serving: TensorRT, ONNX, TorchServe, custom inference servers Orchestration: Kubernetes, Docker, APIGEE, Terraform Data: Vector databases (Pinecone, Weaviate), feature stores, data versioning Specialized Tools Frameworks: Autogen, LangChain, MCP (Model Context Protocol) Evaluation: Custom metrics, human evaluation platforms, A/B testing frameworks Monitoring: MLflow, Weights & Biases, custom dashboards Responsibilities Build text‑to‑image and text‑to‑video generation systems Develop speech synthesis and voice cloning models with safety guardrails for character voices Create image‑to‑text and video‑to‑text systems for content analysis and accessibility Implement cross‑modal generation (text + image? video, audio + text? multimedia content) Build real‑time generative systems for interactive experiences (IoT) Model Evaluation & Quality Assurance Design 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 environments Develop specialized ML pipelines for hallucination detection, bias measurement, and factual accuracy assessment Create domain‑specific evaluation frameworks for use cases (content appropriateness, brand alignment, safety compliance) Implement human‑in‑the‑loop evaluation systems with domain experts Research & Advanced Techniques: Implement cutting‑edge generative AI techniques: diffusion models, transformer variants, mixture of experts Develop constitutional AI and AI safety techniques for responsible content generation Build adversarial training systems to improve model robustness Research and implement prompt engineering and in‑context learning optimization Create novel architectures for specific generative tasks Production AI/ML Systems: Design A/B testing frameworks for generative model comparison and optimization Build real‑time inference optimization for low‑latency content generation Implement model serving infrastructure with auto‑scaling and load balancing Create model monitoring, drift detection, and automatic retraining systems Develop caching and retrieval systems for improved generative AI performance Key Projects & Use Cases (Marketing Content Generation) Build text‑to‑video systems for promotional content creation Develop brand‑consistent image generation with style transfer Create voice synthesis for character‑based marketing campaigns Theme Park Innovation Implement real‑time generative systems for interactive guest experiences Build personalized content generation based on guest preferences Develop safety‑aware content generation for operational communications Customer Experience Enhancement Create personalized response generation for customer support Build multi‑lingual content generation for global audiences Develop accessibility‑focused content generation (audio descriptions, simplified language) Basic Qualifications 5+ years of hands‑on machine learning engineering with 2+ years focused on generative AI Strong experience with transformer architectures, diffusion models, and large language models Proven track record with model fine‑tuning, RLHF, and parameter‑efficient training techniques Experience 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 Expertise Expert‑level Python programming with TensorFlow/PyTorch and distributed training frameworks Experience with cloud ML platforms (GCP Vertex AI, Azure OpenAI, AWS Bedrock) and model serving Strong background in computer vision, NLP, and audio processing for generative applications Knowledge of MLOps, model versioning, and production deployment strategies Experience with vector databases, embeddings, and retrieval‑augmented generation (RAG) AI Safety & Evaluation Experience building evaluation frameworks for generative AI systems Knowledge of AI safety techniques: bias detection, content filtering, adversarial robustness Understanding of responsible AI frameworks and red‑team methodologies Familiarity with AI governance, model interpretability, and compliance requirements Preferred Qualifications Advanced degree in Machine Learning, Computer Science, or related field Experience with industry applications (content creation, media analysis, interactive systems) Knowledge of edge AI optimization and real‑time inference systems Background in reinforcement learning and human preference modeling Experience with large‑scale distributed training (multi‑GPU, multi‑node) Contributions to open‑source AI projects or published research in generative AI Education BE/BS in Machine Learning, Computer Science, or related field #J-18808-Ljbffr 247Hire