Senior Generative AI/Machine Learning Engineer

Job title:- Computer Vision/ Machine Learning Engineer

Location:- FULLY REMOTE JOB

Duration:- 22 Months (Possible for Extension)

Employment Type: W2 Only

Technical Stack

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, Product, 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

Some of the tasks you will be working on

• 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

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• 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 Client 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

Generative AI & Deep Learning:

• 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 teaming 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

Required Education

• Bachelor's Degree in ML, CS, or related field

Feel free to forward my email to your friends/colleagues who might be available. We do offer a referral bonus! Thank you for your time and consideration. I am looking forward to hearing from you.