Senior Artificial Intelligence/Machine Learning Engineer
Tasks
- Architect multi agent networks
- Build reusable agent skills
- Connect AI workflows to enterprise infrastructure
- Decompose workflows into AI agent pipelines
- Deploy AI solutions
- Design system architecture
- Develop AI solutions
- Embed with client teams
- Evaluate AI solutions
- Gather requirements from stakeholders
- Integrate databases
- Integrate third-party tools
- Integrate tools into agents
- Iterate production AI systems
- Lead AI solution lifecycle
- Map business workflows
- Present technical solutions
- Process unstructured documents
- Translate technical concepts for stakeholders
Perks/Benefits
- Company-paid certifications
- Financial consultations
- Language courses
- Legal consultations
- Medical insurance
- Mental health support
- Udemy access
Skills/Tech-stack
AWS | Agent Frameworks | Agent systems | Autogen | CI/CD | Context window | Context window management | CrewAI | Docker | EC2 | ECS | Embeddings | Evaluation | FastAPI | Function Calling | GitHub Actions | Hallucination mitigation | Human-in-the-loop | JWT | JavaScript | LLM API | LLM Agents | Lambda | Langgraph | MCP | Model Context Protocol | Multi-Agent | Multi-Agent Systems | OAuth2 | Observability | OpenSearch | PGVector | Pinecone | Prompt engineering | Python | RAG | RDS | REST APIs | S3 | SQL | Streaming responses | The Loop | Tool use | Vector Databases | Vector Retrieval | Window management
Education
N/A
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