Architect

Opeka Serve; Hyderabad; Hybrid; INR 3,500,000 - 5,200,000; Full time

Job summary

Opeka Serve is hiring a Senior AI/ML Architect in Hyderabad to lead model training, fine-tuning and hosting for enterprise data. You will own end-to-end architecture for training, evaluation and deployment of open-weight LLMs, leveraging PyTorch, Hugging Face, LoRA/QLoRA and PEFT frameworks.

Strong MLOps discipline and cloud deployment experience are essential. You will design scalable, GPU-efficient training and hosting pipelines on AWS/Azure/GCP, implement model serving with vLLM or

Qualifications

• Strong hands-on experience with PyTorch and Hugging Face Transformers.
• Proven experience training and fine-tuning LLM/Deep Learning models using domain data.
• 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, Gemma, DeepSeek, or Phi.
• Strong understanding of training datasets, hyperparameters, GPU utilization, model evaluation, and training optimization.

Responsibilities

• Train and fine-tune open-weight AI/ML models using domain-specific data.
• Design and implement scalable training pipelines and experiment tracking.
• Deploy and host models on cloud platforms with GPU capacity planning.
• Build and maintain model-serving infrastructure (Docker, Kubernetes, vLLM, TensorRT-LLM, etc.).
• Lead ML lifecycle from experimentation to production, including monitoring and rollback.

Skills

PyTorch

Hugging Face

LoRA

QLoRA

PEFT/TRL/Accelerate

LLM Training

GPU/Cloud Infra

MLOps

Python

SQL

Data Pipelines

Model Evaluation

QA/Testing

Tools

AWS

Azure

GCP

Docker

Kubernetes

vLLM/SGLang/TensorRT-LLM/TGI

Job description

Role - 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

1. 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.
• Ability to establish automated pipelines for training, evaluation, deployment, and retraining.
• Strong understanding of production model monitoring and operational management.

Secondary Skills Data & Evaluation

• Python and SQL.
• Experience with large-scale data preparation and data pipelines.
• Experience creating domain-specific evaluation datasets and golden sets.
• Exposure to LLM evaluation, LLM-as-judge, and automated regression testing.
• Experience with Spark or Databricks is a plus.

Model Optimization

• Experience with quantization, GPTQ, AWQ, bitsandbytes, FP8, and other model optimization techniques.
• Understanding of inference optimization including continuous batching, KV-cache optimization, speculative decoding, and parallelism.
• Experience with GPU performance and cost optimization.

AI Engineering

• Exposure to RAG, embeddings, vector databases, agentic AI, tool calling, and MCP.
• Experience with multimodal models is a plus.