AI/ML Ops Engineer

ZK Technologies; Pune District; Hybrid; INR 3,000,000 - 5,400,000; Full time

Job summary

ZK Technologies is seeking an experienced AI/ML MLOps Engineer (L5) to support a high-impact AWS Professional Services engagement focused on finetuning and deploying a self-hosted Large Language Model (LLM). The goal is to replace an existing high-cost commercial inference pipeline with a scalable, in-house solution.

The ideal candidate has deep hands-on expertise in LLM fine-tuning (SFT/DPO), ML serving infrastructure on AWS GPU instances, MLOps CI/CD, and production-grade rollout strategies

Qualifications

• Bachelor's or Master's degree in Computer Science, AI/ML, Data Engineering, or related field.
• 5–8 years hands-on ML engineering, LLM fine-tuning, and MLOps.
• Experience deploying ML/LLM workloads on AWS GPU infrastructure.
• Strong understanding of LLM architectures, training dynamics, and evaluation methodologies.
• Experience with enterprise-grade production deployments and automated rollout strategies.

Responsibilities

• Fine-tune and deploy LLMs on AWS GPU infrastructure.
• Develop and maintain ML serving pipelines and CI/CD for ML workloads.
• Implement canary, shadow-mode, and automated rollback strategies for production deployments.

Skills

Python

LLM Fine-Tuning

Hugging Face Ecosystem

AWS

MLOps CI/CD

LLM Model Serving

Training Data Pipelines

Model Evaluation

Model Quantization

A/B Testing & Canary

Shadow-mode Deployments

Production Monitoring

Automated Rollback

Docker

Kubernetes

EKS

Education

Bachelor's or Master's in CS/AI/ML/Data Eng

Tools

Docker

Kubernetes

EKS

Job description

NOTE: Looking for immediate or short notice period joiners.

Mandatory Skills:

• Python
• LLM Fine Tuning(Qlora/Lora)
• CI/CD Pipeline
• AWS

Overview

We are seeking an experienced AI/ML MLOps Engineer (L5) to support a high-impact AWS Professional Services engagement focused on finetuning and deploying a self-hosted Large Language Model (LLM). The goal is to replace an existing high-cost commercial inference pipeline used for automated UI test execution with a scalable, optimized, and cost-efficient in-house LLM solution.

The ideal candidate has deep hands-on expertise in LLM fine-tuning (SFT/DPO), ML serving infrastructure on AWS GPU instances, MLOps CI/CD, and production-grade rollout strategies including canary, shadow-mode, and automated rollback.

Required Skills & Expertise:

• Python
• LLM Fine-Tuning: SFT, DPO
• Hugging Face Ecosystem: Transformers, Datasets, PEFT
• AWS: GPU/EC2, SageMaker, ML infrastructure
• MLOps & ML CI/CD Pipelines
• LLM Model Serving & Deployment
• Training Data Pipelines
• Model Evaluation & Benchmarking
• Model Quantization (INT8, GPTQ, AWQ)
• A/B Testing & Canary Deployments
• Shadow-mode Deployment Strategies
• Production Model Monitoring
• Automated Rollback Mechanisms
• Docker, Kubernetes, EKS

Qualifications:

• Bachelors or Master’s degree in Computer Science, AI/ML, Data Engineering, or related field.
• 5–8 years of hands-on experience in ML engineering, LLM fine-tuning, and MLOps.
• Proven experience deploying ML/LLM workloads on AWS GPU infrastructure.
• Strong understanding of LLM architectures, training dynamics, and evaluation methodologies.
• Experience working in enterprise-grade production environments with automated deployment strategies.