2 - 4 years
5 - 6 Lacs
Posted:4 weeks ago|
Platform:
Work from Office
Full Time
Role Overview We are seeking a Software Engineer with MLOps skills to contribute to the deployment, automation, and monitoring of GenAI and LLM-based applications . You will work closely with AI researchers, data engineers, and DevOps teams to ensure seamless integration, scalability, and reliability of AI systems in production. Key Responsibilities 1. Deployment & Integration Assist in deploying and optimizing GenAI/LLM models on cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI). Integrate AI models with APIs, microservices, and enterprise applications for real-time use cases. 2. MLOps Pipeline Development Contribute to building CI/CD pipelines for automated model training, evaluation, and deployment using tools like MLflow, Kubeflow, or TFX. Implement model versioning, A/B testing, and rollback strategies. 3. Automation & Monitoring Help automate model retraining, drift detection, and pipeline orchestration (Airflow, Prefect). Assist in designing monitoring dashboards for model performance, data quality, and system health (Prometheus, Grafana). 4. Data Engineering Collaboration Work with data engineers to preprocess and transform unstructured data (text, images) for LLM training/fine-tuning. Support the maintenance of efficient data storage and retrieval systems (vector databases like Pinecone, Milvus). 5. Security & Compliance Follow security best practices for MLOps workflows (model encryption, access controls). Ensure compliance with data privacy regulations (GDPR, CCPA) and ethical AI standards. 6. Collaboration & Best Practices Collaborate with cross-functional teams (AI researchers, DevOps, product) to align technical roadmaps. Document MLOps processes and contribute to reusable templates. Technical Skills Languages: Proficiency in Python and familiarity with SQL/Bash. ML Frameworks: Basic knowledge of PyTorch/TensorFl
Sutherland Global Services Inc
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