AI/ML MLOps Engineer
The MLOps Engineer will manage the end-to-end lifecycle of Large Language Models, including fine-tuning, data pipeline development, and production deployment on AWS. The role focuses on building scalable AI infrastructure using tools like Hugging Face, Docker, and Kubernetes.
- Compensation
- Not disclosed
- Location
- Hyderabad/Pune, India
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 5+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 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
Full description
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