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AIML MLOps Engineer

DATAECONOMY

Job Location:

Hyderabad - India

Monthly Salary: Not provided by the employer

Experience Required: 5-8years

Posted: 1 September 2026 (21 hours ago)

Application Deadline: 29 November 2026

Vacancies: 1 Vacancy

Job Summary

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

Experience: 58 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 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 ones.
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.

Required Skills:

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

Required Education:

Btech/M tech

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