AI Architect

AI Architect (TensorFlow • LLMs • AWS) — Full Ownership Role

Remote · Full-Time · AI/ML Engineering

Important Note (Read Before Applying)

individual project

extensive hands-on experience with AWS

About the Role

AI Architect

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 benefits

Interview Process

-Shortlisting the Candidates

We review applications and screen for required technical experience, niche relevance, and role fit.

-Take-Home Assignment

Short, focused task designed to assess real-world problem-solving, technical depth, and practical execution.

-Technical Call

A deep-dive interview covering hands-on expertise, architecture decisions, AI/ML fundamentals, and project ownership capabilities.

-Final Call

Culture fit + expectations alignment + compensation discussion. Final opportunity for both sides to evaluate mutual fit.