AI Engineer Lead
Tasks
- Apply CI CD for model deployment
- Build and fine-tune foundation model applications
- Build scalable RESTful APIs and microservices
- Collaborate with cross-functional teams
- Deploy manage and optimize AI ML workloads
- Design document chunking and embeddings
- Develop full stack applications integrating GenAI agents
- Develop scalable Python applications for GenAI
- Follow coding best practices with testing and documentation
- Implement LLMOps and MLOps practices
- Implement Model Context Protocol solutions
- Implement retrieval-augmented generation
- Integrate agentic AI frameworks
- Lead autonomous agent and multi agent system implementation
- Monitor model and application lifecycle
- Perform prompt engineering
- Research and experiment with generative AI advancements
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agent Frameworks | Agent systems | Angular | Asyncio | Autonomous Agents | Azure | CI/CD | CSS | Cassandra | Django | Document chunking | Embeddings | FastAPI | Flask | GCP | Go | HTML | Haystack | JavaScript | LLMOps | Langchain | Language Models | Large Language Models | Llamaindex | MCP | MLOps | Microservices | Model Context Protocol | Model Deployment | MongoDB | Monitoring | Multi-Agent | Multi-Agent Systems | Multi-agent frameworks | Multimodal Models | MySQL | NoSQL | Node.js | NumPy | Object-Oriented | Object-oriented programming | Pandas | PostgreSQL | Prompt engineering | PyTorch | Python | REST APIs | React | Retrieval-Augmented Generation | SQL | Semantic Kernel | Snowflake | Snowflake Cortex | TensorFlow | TypeScript | Vue.js
Education
N/A
Roles
AI | AI Engineer | Engineer | GenAI Engineer
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