AI/ML & Forward Deployed Engineer
Tasks
- Build and optimize machine learning models for deep learning NLP forecasting classification regression anomaly detection
- Build scalable AI services using REST and gRPC APIs and event driven architectures
- Containerize and orchestrate AI applications using Docker and Kubernetes and integrate into CI CD pipelines
- Design develop deploy machine learning and GenAI solutions from proof of concept to production
- Develop GenAI applications using large language models and retrieval augmented generation pipelines
- Ensure data quality governance security RBAC encryption and auditability
- Establish MLOps and LLMOps practices for deployment automation versioning monitoring and lifecycle management
- Implement AI evaluation frameworks for quality reliability relevance and performance
- Implement model monitoring drift detection and performance tracking for continuous improvement
- Monitor troubleshoot and optimize AI systems for production operations
Perks/Benefits
- Collaborative stakeholder engagement
- Competitive salary
- Hands on exposure to LLMs RAG MLOps and Kubernetes
- Remote working model
- Work on cutting-edge AI solutions
Skills/Tech-stack
Access Control | Anomaly Detection | CI/CD | Classification | Data Governance | Deep learning | Docker | Drift Detection | Embeddings | Encryption | Event Driven | Event-driven architecture | Forecasting | GRPC | Kubernetes | LLMOps | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Drift | Model Drift Detection | Model Evaluation | Model Monitoring | Natural Language | Natural Language Processing | Prompt engineering | Python | REST API | Regression | Retrieval-Augmented Generation | Role-Based Access | Role-Based Access Control | Vector Search
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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