AI/ML & Forward Deployed Engineer
Tasks
- Build and optimize ML models for deep learning NLP forecasting classification regression and anomaly detection
- Build scalable AI services with REST and gRPC APIs and event driven architectures
- Containerize and orchestrate AI applications with Docker and Kubernetes using CI CD pipelines
- Design deploy machine learning AI and GenAI solutions from proof of concept to production
- Develop production GenAI applications using large language models and RAG pipelines
- Ensure governance security RBAC encryption and auditability for enterprise requirements
- Establish MLOps and LLMOps practices for deployment automation versioning monitoring and lifecycle management
- Implement AI evaluation frameworks for model quality reliability relevance and performance
- Implement model monitoring drift detection and performance tracking for continuous improvement
- Monitor troubleshoot and optimize AI solutions in production operations
Perks/Benefits
- Collaborative stakeholder environment
- Hands on AI engineering
- Health and governance focused projects
- Remote working model
Skills/Tech-stack
Access Control | Anomaly Detection | CI/CD | Classification | 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 | Observability | Prompt engineering | Python | REST APIs | Regression | Reranking | Retrieval-Augmented Generation | Role-Based Access | Role-Based Access Control
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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