MLOps Engineer

We are looking for a talented and driven MLOps Engineer to join our AI/ML team. In this role, you will design and build scalable ML infrastructure, MLOps pipelines, and CI/CD workflows for deploying and managing NLP, generative AI, and computer vision models.

Responsibilities:

• ML Infrastructure and Platform: Design, build, and maintain scalable MLOps pipelines and infrastructure for training, deploying, and managing NLP, GenAI, and computer vision models.
• CI/CD for Machine Learning: Implement automated continuous integration and continuous deployment (CI/CD) pipelines for ML models, ensuring smooth transitions from development to production environments.
• Model Serving and Deployment: Lead the deployment and lifecycle management of Large Language Models (LLMs), Vision-Language Models (VLMs), and other ML models using scalable serving engines (e. g., vLLM, Triton Inference Server, Ray Serve).
• Monitoring and Observability: Set up comprehensive monitoring frameworks to track model performance, data drift, resource utilization, and system health in real-time production environments.
• Pipeline Orchestration: Develop and manage end-to-end ML workflows encompassing data ingestion, preprocessing, training, evaluation, and inference using tools like Kubeflow, MLflow, or Apache Airflow.
• Cloud and Resource Optimization: Optimize cloud infrastructure (AWS/GCP/Azure) for cost, latency, and throughput. Ensure efficient hardware resource allocation across GPUs and distributed computing clusters.
• Cross-Functional Collaboration: Collaborate closely with data scientists, NLP/CV engineers, and software engineers to define hardware constraints, capacity planning, and deployment strategies.

Requirements:

• Qualification: BTech in CS/AI/Data Science or related discipline.
• Possess 2-5 years of professional experience in MLOps, machine learning engineering, or closely related technical domains.
• Demonstrated expertise in the deployment and lifecycle management of Large Language Models (LLMs), VLMs, and diverse ML architectures within production-grade environments.
• High proficiency with MLOps and serving frameworks, including MLflow, Kubeflow, vLLM, Triton Inference Server, and TensorRT for high-performance inference.
• Practical experience implementing vector databases (such as Pinecone, FAISS, or Weaviate) alongside robust CI/CD tooling like GitHub Actions, Jenkins, or Terraform.
• Advanced coding skills in Python, C++, and Bash, with a strong background in managing containerized workloads using Docker and Kubernetes across major cloud platforms (AWS, GCP, or Azure).
• Comprehensive understanding of ML pipelines and observability, including model versioning, registries, and monitoring frameworks like Prometheus and Grafana for tracking system health and data drift.
• Excellent communication abilities with a collaborative mindset and a focus on solving complex technical challenges.