AI/ML MLOps Engineer

Data Economy; Pune District; Hybrid; INR 1,800,000 - 2,400,000; Full time

Benefits offered by this job

Health insurance

Group accident insurance

Group term life insurance

Hybrid work

21 days annual leave

Refreshment lounge

Job summary

Data Economy is seeking an experienced AI/ML MLOps Engineer to lead LLM fine-tuning, deployment, and production ML workflows. You will build data pipelines, optimize inference, and implement robust MLOps practices in a hybrid environment.

Ideal candidates have hands-on SFT/DPO, Hugging Face expertise, and experience with AWS GPU infrastructure and SageMaker. Join a cross-functional team delivering scalable AI solutions.

Qualifications

• 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.

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.

Skills

Python

LLM fine-tuning

SFT

DPO

Hugging Face Transformers

Hugging Face Datasets

PEFT

AWS SageMaker

MLOps

CI/CD

Docker

Kubernetes

Tools

AWS EC2

SageMaker

Docker

Kubernetes

Job description

Job Summary

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

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.

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 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: 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.