Lead AI Consultant
Tasks
- Architect RAG pipelines with embeddings vector databases and semantic retrieval
- Build and deploy machine learning and NLP solutions
- Build hybrid semantic and keyword search solutions
- Build scalable AI services with microservices and APIs
- Deploy secure observable and scalable production AI platforms
- Design model training validation evaluation and monitoring pipelines
- Design multi agent AI architectures and agent orchestration
- Develop agent memory planning tool integration and execution frameworks
- Develop enterprise LLM powered applications
- Establish AI governance model lifecycle and responsible AI best practices
- Implement monitoring logging tracing and operational controls
- Implement prompt engineering orchestration and evaluation frameworks
- Lead design architecture and implementation of LLM AI platforms
- Provide technical leadership mentorship code reviews and architecture reviews
Perks/Benefits
- N/A
Skills/Tech-stack
Agent Orchestration | Agent systems | Amazon Web Services | Azure | CI/CD | Cloud Native | Cloud platform | Container Platform | Distributed Systems | Docker | Drift Detection | Elasticsearch | Embeddings | Faiss | Feature Engineering | Fine Tuning | GRPC | Generative AI | Google Cloud | Google Cloud Platform | Kubernetes | LLM | Language Processing | ML CI/CD | MLOps | Machine Learning | Microservices | Model Evaluation | Model Governance | Model Monitoring | Multi-Agent | Multi-Agent Systems | Natural Language | Natural Language Processing | OpenSearch | OpenShift Container Platform | Prompt engineering | PyTorch | Python | REST API | Retrieval-Augmented Generation | Semantic Search | TensorFlow | Tool-Calling | Vector Databases | Web Services
Education
N/A
Roles
AI | AI Consultant | AI Engineer | Consultant | Engineer | Lead | Lead AI Engineer
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