Generative AI Solutions 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 DoArchitect, 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 For5+ 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 HaveExperience 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.BenefitsCompetitive salaryHybrid environment with remote work flexibilityProfessional development budgetLatest hardware, GPUs, and toolingComprehensive 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.