AI/ML & Forward Deployed Engineer
Tasks
- Build and optimize machine learning models for NLP forecasting classification regression and anomaly detection
- Build scalable AI services with REST and gRPC APIs and event driven architectures
- Collaborate with stakeholders to translate business needs into practical AI solutions and measurable outcomes
- Containerize and orchestrate AI applications with Docker and Kubernetes and integrate CI CD pipelines
- Design and implement AI evaluation frameworks for model quality reliability relevance and performance
- Design develop and deploy machine learning and AI solutions from proof of concept through production
- Develop generative AI applications using large language models and retrieval augmented generation
- Ensure AI solutions meet data quality governance security RBAC encryption and auditability requirements
- Establish and maintain MLOps and LLMOps practices for deployment automation versioning monitoring and lifecycle management
- Implement model monitoring drift detection and performance tracking for continuous model improvement
- Support production operations troubleshooting optimization and continuous improvements
Perks/Benefits
- Collaborative stakeholder driven environment
- Exposure to LLMs RAG MLOps and Kubernetes
- Focus on scalability security governance and observability
- Hands on AI and machine learning projects
- Remote work model
Skills/Tech-stack
Access Control | Anomaly Detection | Auditability | 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 | Prompt engineering | Python | REST APIs | Regression | Retrieval-Augmented Generation | Role-Based Access | Role-Based Access Control
Education
N/A
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