Forward Deployed AI Engineer
Tasks
- Build RAG and retrieval pipelines
- Design and build production agentic AI systems
- Embed with client environment
- Engineer data ingestion and enrichment pipelines
- Ensure security and compliance
- Establish evaluation datasets and automated grading
- Implement observability and tracing
- Implement tool and function calling
- Measure retrieval quality groundedness faithfulness
- Optimize cost and latency for AI workloads
- Orchestrate multi-agent workflows
- Set up CI/CD and infrastructure-as-code
- Transfer knowledge with client engineers
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agent Orchestration | Agent SDK | Agent systems | Amazon Bedrock | Anthropic Agent SDK | Application engineering | Atlas Search | Atlas Vector Search | Azure | CI/CD | Caching | Cloud platform | Code review | Containers | Context Management | Data Enrichment | Data Ingestion | Embeddings | Function Calling | Git | Google Cloud | Google Cloud Platform | Human-in-the-loop | Hybrid search | IAM | Infrastructure as Code | Interrupts | LLM Evaluation | LLM application | LLM application engineering | LLM-as-judge | Langgraph | Llamaindex | MongoDB | Multi-Agent | Multi-Agent Systems | Natural Language | Natural Language to Query | Networking | Node.js | Object storage | Observability | Planner router patterns | Prompt engineering | Python | RAG | Re-ranking | Regression testing | Retries | Retrieval-Augmented Generation | SQL | Secrets management | Semantic Search | Serverless | State checkpointing | Streaming | Structured Output | Terraform | Testing | The Loop | Token Management | Token budgeting | Tool-Calling | Tracing | TypeScript | Vector Search | “as-code”
Education
N/A
Roles
AI | AI Engineer | Deployed AI Engineer | Engineer | Forward Deployed AI Engineer
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