AI/ML MLOps Engineer

Job Title: AI/ML MLOps Engineer – LLM Fine-Tuning & Deployment

Experience: 5–8 Years

Location: Hyderabad

Employment Type: Full-Time, Hybrid

We are looking for an experienced AI/ML MLOps Engineer with strong hands-on expertise in LLM fine-tuning, model deployment, AWS GPU infrastructure, and MLOps. The role involves fine-tuning and deploying self-hosted Large Language Models (LLMs), building training and evaluation pipelines, and implementing reliable production deployment and monitoring practices.The ideal candidate should have practical experience working across the complete ML lifecycle — data preparation, model fine-tuning, evaluation, deployment, monitoring, and continuous improvement.

Key Responsibilities

​

• Fine-tune Large Language Models using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO).
• Develop and maintain training data pipelines, including data transformation, formatting, deduplication, filtering, and quality validation.
• Work extensively with the Hugging Face ecosystem, including Transformers, Datasets, and PEFT.
• Build and automate model evaluation and benchmarking frameworks to assess model quality and performance.
• Deploy and serve LLM models using AWS GPU/EC2 infrastructure and Amazon SageMaker.
• Optimize models for production through model quantization, inference optimization, and resource utilization.
• Build robust MLOps and ML CI/CD pipelines covering model training, evaluation, packaging, deployment, and monitoring.
• Implement A/B testing, Canary, and Shadow-mode deployments for safely introducing new model versions into production.
• Develop mechanisms for automated model promotion and rollback based on predefined performance and operational metrics.
• Implement production monitoring for model performance, latency, throughput, errors, GPU utilization, and resource consumption.
• Containerize ML workloads using Docker and deploy/manage them using Kubernetes/Amazon EKS.
• Collaborate with Data Scientists, ML Engineers, DevOps teams, and other stakeholders to build scalable and reliable AI/ML solutions.

Requirements

• Strong programming experience in

Python

.

• Hands-on experience with

LLM fine-tuning

, particularly

SFT and DPO

.

• Strong knowledge of

Hugging Face Transformers, Datasets, and PEFT

.

• Experience working with

AWS GPU/EC2 and SageMaker

for ML workloads.

• Strong understanding of

MLOps, ML CI/CD, and model lifecycle management

.

• Experience with

LLM model serving and production deployment

.

• Experience building

training data preparation and processing pipelines

.

• Knowledge of

model evaluation, benchmarking, and performance optimization

.

• Hands-on experience with

model quantization

.

• ​Experience implementing

A/B, Canary, and Shadow-mode deployments

Benefits

• Comprehensive Medical Coverage:

  Health insurance of INR 7.0 Lakhs for you and your family (up to 6 members), ensuring complete peace of mind.

• Robust Protection Plans:

  Group Personal Accident Insurance and Group Term Life Insurance to safeguard you and your loved.

• Retirement Benefits:

  PF and Gratuity provided as per standard government regulations.

• Flexible Work Options:

  Enjoy hybrid work arrangements & flexible working hours

• Generous Leave Policy:

  21 days of annual leave, in addition to 10 company-declared holidays.

• Employee Well-being Spaces:

  Access to a dedicated break-out area with round-the-clock refreshments for relaxation and rejuvenation.

• Strong programming experience in Python
• Hands-on experience with LLM fine-tuning, particularly SFT and DPO
• Strong knowledge of Hugging Face Transformers, Datasets, and PEFT
• Experience working with AWS GPU/EC2 and SageMaker for ML workloads
• Strong understanding of MLOps, ML CI/CD, and model lifecycle management
• Experience with LLM model serving and production deployment
• Experience building training data preparation and processing pipelines
• Knowledge of model evaluation, benchmarking, and performance optimization
• Hands-on experience with model quantization
• Experience implementing A/B, Canary, and Shadow-mode deployments