AI Architect

AI Architect (TensorFlow• LLMs• AWS) — Full Ownership RoleRemote · Full-Time · AI/ML EngineeringImportant Note (Read Before Applying)This is an individual project role — not a team-managed or shared workload position. You will hold full ownership, end-to-end responsibility, and complete accountability for architecting, building, training, fine-tuning, and deploying advanced AI models.Only apply if you have extensive hands-on experience with AWS, LLM fine-tuning, and TensorFlow-based model development, and you are fully capable of independently delivering production-grade AI systems.About the RoleWe are seeking an exceptional AI Architect with deep expertise in TensorFlow, Large Language Model fine-tuning, and AWS cloud infrastructure. You will architect next-generation AI systems, develop advanced NLP and generative models, and build scalable training and deployment pipelines from scratch.This is a high-impact position for someone who excels in independent work, takes full ownership, and can deliver complex ML systems without supervision.What You'll Do- Architect, design, and lead complete AI/ML systems with full end-to-end ownership.- Build, train, fine-tune, and optimize deep learning models using TensorFlow 2.x and TFX.- Develop and fine-tune Large Language Models (LLMs) for domain-specific applications.- Implement complete NLP pipelines including classification, NER, sentiment analysis, summarization, and generation.- Build distributed training systems optimized for multi-GPU/TPU compute.- Deploy and manage models on AWS SageMaker, AWS Bedrock, and supporting AWS infrastructure.- Establish and maintain MLOps frameworks for CI/CD, retraining, monitoring, and model versioning.- Optimize training throughput and model architecture for speed, accuracy, and compute efficiency.- Conduct deep performance benchmarking, validation, and model evaluation.- Build scalable, automated training data pipelines alongside data engineering assets.- Mentor internal team members on TensorFlow, LLM training, and AWS-based ML systems.What We're Looking For- 5+ years experience in ML engineering, AI architecture, or deep learning model development.- Expert-level proficiency with TensorFlow 2.x, Keras, and TFX pipelines.- Demonstrated experience fine-tuning large-scale LLMs from scratch or via parameter-efficient methods (LoRA, QLoRA, adapters).- Strong expertise with NLP, Transformers, attention mechanisms, and modern model architectures.- Extensive hands-on experience with AWS cloud services — SageMaker, Bedrock, EC2 GPU instances, S3, IAM, Lambda.- Strong understanding of training optimization techniques (learning rate schedules, mixed precision, gradient accumulation).- Experience with distributed training frameworks, multi-GPU/TPU training, and scaling model training workloads.- Strong Python programming skills (NumPy, Pandas, scikit-learn, data preprocessing).- Knowledge of model compression (quantization, pruning, distillation) for deployment efficiency.- Ability to independently deliver full AI solutions with minimal direction.Nice to Have- Experience with Hugging Face Transformers or LangChain.- Familiarity with PyTorch or JAX.- Experience with RLHF, human feedback loops, or preference modeling.- Experience with vector databases (Pinecone, Weaviate, ChromaDB) for RAG workflows.- Knowledge of few-shot prompting, prompt engineering, or LLM orchestration.- AWS ML Specialty or Solutions Architect certification.- Experience with Docker/Kubernetes for ML deployment.- Published ML research or open-source contributions.- Master's or PhD in CS, ML, or a related field.Benefits- Competitive salary- Hybrid environment with remote work flexibility- Professional development budget- Latest hardware, GPUs, and tooling- Comprehensive health and wellness benefitsInterview Process-Shortlisting the CandidatesWe review applications and screen for required technical experience, niche relevance, and role fit.-Take-Home AssignmentShort, focused task designed to assess real-world problem-solving, technical depth, and practical execution.-Technical CallA deep-dive interview covering hands-on expertise, architecture decisions, AI/ML fundamentals, and project ownership capabilities.-Final CallCulture fit + expectations alignment + compensation discussion. Final opportunity for both sides to evaluate mutual fit.