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
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Engineering - Software & QARole Category: DevOpsRole: DevOps EngineerEmployement Type: Full time