The Role
Build and manage the full lifecycle for self-hosted LLMs, including training data pipelines, SFT and DPO fine-tuning, evaluation, benchmarking, quantization, deployment, monitoring, and continuous improvement. Deploy models on AWS GPU infrastructure and SageMaker, automate ML CI/CD, and manage Docker and Kubernetes workloads on EKS. Implement A/B, canary, and shadow deployments with automated promotion and rollback based on performance and operational metrics. Collaborate with data science, ML engineering, and DevOps teams.
Summary Generated by Built In
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 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.
Skills Required
- Strong programming experience in Python
- Hands-on experience with LLM fine-tuning, particularly Supervised Fine-Tuning and Direct Preference Optimization
- Strong knowledge of Hugging Face Transformers, Datasets, and PEFT
- Experience with AWS GPU or EC2 infrastructure and Amazon 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
- Five to eight years of experience
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The Company
What We Do
DATAECONOMY is a global, cloud-first data and AI consultancy delivering enterprise-grade solutions through an innovative intellectual-property suite. Its work spans data and BI platform modernization, self-service AI, data mesh and fabric, master data management, governance, cloud enablement, digital engineering, knowledge graphs, and machine lakes supporting cybersecurity and financial-crime use cases for enterprise clients.
