Architect

Interested candidate can share there Resume @ Kavita@opekaserve.comRole - AI/ML Architect
Exp 10 to 18 Years

Job Location Hyderabad (3 days Work from Office, Hybrid)
Mode of Hiring FTE/Permanent

Notice Period Max 15th Nov 2026

Senior AI / ML Architect Model Training, Fine-Tuning & Hosting Job Summary We are seeking a Senior AI / ML Architect with 1015 years of experience and strong hands-on expertise in machine learning, deep learning, LLM model training, domain-specific fine-tuning, cloud deployment, and model hosting. The primary focus of this role will be to train and fine-tune open-weight AI/ML models using domain specific enterprise data and build scalable infrastructure to host, serve, manage, and monitor these models in production. This is a highly hands-on role requiring strong practical experience with PyTorch, Hugging Face, LoRA, QLoRA, GPU infrastructure, cloud platforms, model serving, and MLOps tools. The candidate should be an experienced technical architect who can make architecture decisions while remaining deeply involved in implementation and troubleshooting.

Primary Skills

• Model Training & Fine-Tuning Non-Negotiable

• Strong hands-on experience with PyTorch and Hugging Face Transformers.

• Proven experience training and fine-tuning LLM/Deep Learning models using domain specific data.

• Strong hands-on expertise in LoRA and QLoRA.

• Experience with PEFT, SFT, TRL, Accelerate, or equivalent frameworks.

• Experience with open-weight models such as Llama, Qwen, Mistral/Mixtral, Gemma, DeepSeek, or Phi.

• Strong understanding of training datasets, hyperparameters, GPU utilization, model evaluation, and training optimization. 2. Model Hosting & Cloud Deployment — Non-Negotiable

• Strong hands-on experience deploying and hosting ML/LLM models on AWS, Azure, or GCP.

• Experience with GPU-based cloud infrastructure and capacity planning.

• Hands-on experience with Docker and Kubernetes.

• Experience with model serving frameworks such as vLLM, SGLang, TensorRT-LLM, or TGI.

• Ability to design scalable and reliable model-serving infrastructure for production workloads. 3. MLOps & Model Lifecycle Management — Non-Negotiable

• Hands-on experience implementing ML/LLM lifecycle management from experimentation through production.

• Experience with MLflow, W&B, model registries, CI/CD, model versioning, deployment, monitoring, and rollback.