AI ML MLOps Engineer

DATAECONOMY; Hyderabad; Hybrid; INR 2,400,000 - 4,200,000; Full time

Benefits offered by this job

Health insurance: INR 7.0 Lakhs

Group Personal Accident Insurance

Group Term Life Insurance

PF and Gratuity

Hybrid work arrangements

Flexible working hours

21 days annual leave + 10 holidays

Well-being break-out area

Job summary

DATAECONOMY in Hyderabad is seeking an AI/ML MLOps Engineer to fine-tune LLMs (SFT & DPO), build data pipelines, and deploy models on AWS SageMaker. The role requires 5–8 years of experience and a strong foundation in Python and Hugging Face tools.

You will implement MLOps pipelines, perform A/B/Canary/Shadow deployments, and containerize workloads with Docker and Kubernetes, collaborating with Data Scientists and DevOps teams to deliver scalable AI solutions.

Qualifications

• Proficient in Python programming with ML focus.
• Hands-on experience fine-tuning LLMs (SFT/DPO).
• Strong knowledge of Hugging Face tools (Transformers, Datasets, PEFT).
• Experience with AWS GPU/EC2 and SageMaker for ML workloads.
• Familiarity with MLOps, CI/CD, and model lifecycle management.
• Experience deploying LLMs to production environments.
• Data preparation and processing pipelines

Responsibilities

• Fine-tune LLMs using SFT and DPO.
• Develop training data pipelines with quality validation.
• Leverage Hugging Face ecosystem for model development.
• Build evaluation and benchmarking frameworks.
• Deploy models on AWS SageMaker and EC2 infrastructure.
• Optimize models for production via quantization and tuning.
• Create MLOps/CI-CD pipelines for training, deployment, monitoring.
• Implement A/B, Canary, and Shadow deployments.
• Automate model promotion and rollback based on metrics.
• Monitor production performance including latency and GPU usage.
• Containerize workloads with Docker; manage via Kubernetes/EKS.

Skills

Python

LLM fine-tuning

SFT

DPO

Model evaluation

Training data pipelines

QA/data quality

Tools

Hugging Face Transformers

Hugging Face Datasets

PEFT

SageMaker

EC2

Docker

Kubernetes

Job description

Job Description:

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

Experience: 5–8 Years

Location: Hyderabad

Employment Type: Full-Time, Hybrid

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.