AI/ML & Forward Deployed Engineer
Tasks
- Automate deployment monitoring and lifecycle management
- Build optimize deep learning NLP forecasting classification regression anomaly detection models
- Build scalable AI services with REST and gRPC
- Collaborate with stakeholders to translate requirements into AI solutions
- Containerize and orchestrate with Docker and Kubernetes
- Design deploy AI ML GenAI solutions
- Develop GenAI applications with LLMs and RAG pipelines
- Enforce data quality governance security RBAC encryption and auditability
- Establish MLOps and LLMOps pipelines
- Implement AI evaluation frameworks
- Implement event-driven architectures
- Implement model monitoring drift detection and performance tracking
- Integrate CI/CD pipelines
- Support production operations troubleshooting and optimization
Perks/Benefits
- Competitive salary
- Exposure to LLM RAG MLOps LLMOps Kubernetes
- Hands on AI ML GenAI projects
- Remote working model
- Scalable secure governed observable engineering environment
- Stakeholder collaboration
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 Monitoring | Model versioning | Natural Language | Natural Language Processing | Observability | Prompt engineering | Python | RAG | REST APIs | Regression | Reranking | Retrieval optimization | Retrieval-Augmented Generation | Role-Based Access | Role-Based Access Control
Education
N/A
Roles
AI | AI Engineer | Engineer | LLM Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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