AI/ML & Forward Deployed Engineer
Tasks
- Build and optimize machine learning models for NLP forecasting classification regression and anomaly detection
- Build scalable AI services using REST and gRPC and event driven architectures
- Collaborate with stakeholders to translate business needs into technical solutions
- Containerize and orchestrate AI applications using Docker and Kubernetes and integrate into CI CD pipelines
- Design and develop machine learning and generative AI solutions from proof of concept to production
- Develop retrieval augmented generation applications using embeddings and reranking
- Ensure AI solutions meet governance security data quality RBAC encryption and auditability requirements
- Establish and maintain MLOps and LLMOps practices for deployment automation versioning and monitoring
- Implement AI evaluation frameworks for model quality reliability relevance and performance
- Implement model monitoring drift detection and performance tracking for continuous improvement
Perks/Benefits
- Collaborative environment with stakeholder engagement
- Focus on scalability security governance and observability
- Hands on exposure to LLMs RAG MLOps and Kubernetes
- Opportunities to work on cutting-edge AI
- Remote work model
Skills/Tech-stack
Anomaly Detection | Audit Logging | CI/CD | Classification | Deep learning | Docker | Drift Detection | Embeddings | Encryption | Event Driven | Event-driven architecture | Forecasting | GRPC | Generative AI | Kubernetes | LLMOps | LLMs | Language Processing | MLOps | Machine Learning | Model Drift | Model Drift Detection | Model Monitoring | Model versioning | NLP | Natural Language | Natural Language Processing | Prompt engineering | Python | RAG | RBAC | REST API | Regression | Reranking | Retrieval-Augmented Generation | Vector Search
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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