AI/ML MLOps Engineer

DATAECONOMY; Hyderabad, Pune District; Hybrid; INR 1,600,000 - 2,200,000; Full time

Benefits offered by this job

Health insurance

Group Personal Accident Insurance

Group Term Life Insurance

PF & Gratuity

Hybrid work arrangements

21 days annual leave

10 holidays

Job summary

DATAECONOMY is seeking an experienced AI/ML MLOps Engineer to fine-tune and deploy self-hosted LLMs, build data pipelines, and implement robust production monitoring. The role covers the full ML lifecycle from data prep to deployment and continuous improvement.

The candidate will work with Hugging Face tools, AWS GPU/EC2, and SageMaker, delivering reliable MLOps pipelines, model evaluation, and scalable deployment strategies.

Qualifications

• 5–8 years of hands-on experience in AI/ML, MLOps, and production deployment.
• Strong Python programming and deep learning workflow expertise.
• Practical experience with fine-tuning LLMs and deploying them in production.
• Hands-on experience with HuggingFace ecosystem and PEFT tools.

Responsibilities

• Fine-tune LLMs using SFT and DPO techniques.
• Build and maintain training data pipelines: transform, deduplicate, validate quality.
• Work with Hugging Face Transformers, Datasets, and PEFT libraries.
• Create model evaluation and benchmarking frameworks for quality checks.
• Deploy and serve LLMs on AWS GPU/EC2 and SageMaker.
• Optimize models for production via quantization and performance tuning.
• Develop MLOps and ML CI/CD pipelines for training, evaluation, packaging, deployment, monitoring.
• Implement A/B testing, Canary, and Shadow deployments for safe rollouts.
• Automate model promotion and rollback using performance metrics.
• Establish production monitoring for latency, throughput, errors, and GPU usage.
• Containerize workloads with Docker and orchestrate with Kubernetes/Amazon EKS.
• Collaborate with data scientists, ML engineers, and DevOps to build scalable ML solutions.

Skills

Python

LLM fine-tuning

HuggingFace Transformers

Datasets

PEFT

AWS GPU/EC2

SageMaker

MLOps

CI/CD

Model lifecycle

Model serving

Deploy pipelines

A/B testing

Canary/Shadow deployments

Docker

Kubernetes

EDI data pipelines

Tools

Docker

Kubernetes

Amazon EKS

SageMaker

AWS EC2

Job description

Hyderabad/Pune, India | Posted on 08/31/2026

Experience: 5–8 Years

Employment Type: Full-Time, Hybrid

We are looking for an experienced AI/ML MLOps Engineer withstrong hands-on expertise in LLM fine-tuning, model deployment, AWS GPUinfrastructure, and MLOps. The role involves fine-tuning and deployingself-hosted Large Language Models (LLMs), building training and evaluationpipelines, and implementing reliable production deployment and monitoringpractices.The ideal candidate should have practical experience working across thecomplete ML lifecycle — data preparation, model fine-tuning, evaluation,deployment, monitoring, and continuous improvement.

Key Responsibilities

• Fine-tune LargeLanguage Models using Supervised Fine-Tuning (SFT) and DirectPreference Optimization (DPO).
• Develop and maintain training data pipelines, including data transformation, formatting,deduplication, filtering, and quality validation.
• Work extensivelywith the Hugging Face ecosystem, including Transformers, Datasets,and PEFT.
• Build and automate modelevaluation and benchmarking frameworks to assess model quality andperformance.
• Deploy and serve LLMmodels using AWS GPU/EC2 infrastructure and Amazon SageMaker.
• Optimize models forproduction through model quantization, inference optimization, andresource utilization.
• Build robust MLOpsand ML CI/CD pipelines covering model training, evaluation, packaging,deployment, and monitoring.
• Implement A/Btesting, Canary, and Shadow-mode deployments for safely introducingnew model versions into production.
• Develop mechanismsfor automated model promotion and rollback based on predefinedperformance and operational metrics.
• Implement productionmonitoring for model performance, latency, throughput, errors, GPUutilization, and resource consumption.
• Containerize MLworkloads using Docker and deploy/manage them using Kubernetes/AmazonEKS.
• Collaborate withData Scientists, ML Engineers, DevOps teams, and other stakeholders tobuild scalable and reliable AI/ML solutions.

Requirements

• Strong programmingexperience in Python .
• Hands-on experiencewith LLM fine-tuning , particularly SFT and DPO .
• Strong knowledge of HuggingFace Transformers, Datasets, and PEFT .
• Experience workingwith AWS GPU/EC2 and SageMaker for ML workloads.
• Strong understandingof MLOps, ML CI/CD, and model lifecycle management .
• Experience with LLMmodel serving and production deployment .
• Experience building trainingdata preparation and processing pipelines .
• Knowledge of modelevaluation, benchmarking, and performance optimization .
• Hands-on experiencewith model quantization .
  
• Experience implementing A/B, Canary, andShadow-mode deployments

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.

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.

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