Senior AI Engineer
New
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- DockerPythonApache AirflowMachine LearningMLFlowAzureDeep LearningMLOpsGenerative AI
Requirements
- 5+ years of experience in AI/ML engineering, including at least 2+ years working on GenAI or LLM-based systems.
- Strong proficiency in Python for machine learning, automation, and production-grade AI system development.
- Hands-on experience building GenAI applications such as RAG pipelines or AI agents, including evaluation and guardrail design.
- Proven experience deploying and operating ML models in production with monitoring, retraining, and observability frameworks.
- Experience with cloud platforms, preferably Azure (Azure ML), with exposure to OCI considered a plus.
- Strong understanding of MLOps practices, including CI/CD pipelines and tools such as MLflow.
- Experience containerizing and deploying services using Docker and cloud-native services such as Azure App Service or Azure Container Apps.
- Familiarity with orchestration tools such as Apache Airflow or Azure Data Factory / Azure ML pipelines.
- Good understanding of scalable system design principles, including latency optimization, caching, batching, and rate limiting strategies.
- Experience with deep learning frameworks such as PyTorch or TensorFlow is a plus.
- Strong communication skills and ability to collaborate effectively in cross-functional engineering teams.
Responsibilities
- Design, build, and deploy end-to-end AI solutions using machine learning, deep learning, and generative AI techniques based on business and product requirements.
- Develop and maintain scalable pipelines for training, evaluation, deployment, monitoring, and automated retraining of AI models.
- Build and productionize GenAI applications such as RAG systems and agent-based workflows with robust evaluation frameworks and safety guardrails.
- Deploy AI services for real-time and batch inference, ensuring performance, scalability, and cost efficiency.
- Implement model observability practices including performance tracking, drift detection, data quality monitoring, alerting, and production troubleshooting.
- Design and support multi-tenant AI solutions that can be safely reused across multiple customers with consistent performance and governance.
- Collaborate with platform, DevOps, and engineering teams to integrate AI systems into CI/CD pipelines and production environments.
- Define and enforce best practices for model lifecycle management, versioning, and automated retraining workflows.
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