AI Forward Deployed Engineer

Voto Consulting LLC; Houston (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Voto Consulting LLC is seeking an Architecture Design lead to build enterprise Generative and Agentic AI platforms featuring high-performance RAG pipelines and vector database integrations.

You will define agent orchestration, memory management, and autonomous planning frameworks, while implementing governance and security guardrails. You will mentor engineering teams and shape reusable deployment patterns across cloud-native environments.

Job description

• Architecture Design: Build enterprise Generative and Agentic AI platforms featuring high-performance RAG (Retrieval-Augmented Generation) pipelines and vector database integrations.
• Agent Orchestration: Define multi-agent collaboration patterns, memory management, and autonomous planning frameworks.
• Governance & Security: Implement data privacy, compliance, risk mitigation, and evaluation guardrails across all AI touchpoints.
• Cross-functional Leadership: Guide and mentor engineering teams, run discovery workshops with stakeholders, and define reusable deployment patterns.

Technical Stack

• Retrieval-Augmented Generation (RAG) pipelines, semantic caching, and context window optimization.
• Function calling, tool use, and structured data extraction schemas.
• Evaluation metrics, tracing, and hallucination reduction guardrails.
• Designing agentic first workflows and autonomous decision loops.
• Frameworks like LangChain/LangGraph/Bedrock Core Runtime for state and memory management.
• Emerging interoperability standards like Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols.
• Vector databases (e.g., Milvus, Amazon Aurora PostgreSQL) for high-speed similarity search.
• Data pipelines and embedding generation workflows using Python, FastAPI, or Apache Spark.
• Cloud-native deployment on platforms like AWS (Amazon Bedrock, Lambda, EKS, SageMaker, S3, RDS, DocumentDB)
• Containerization and orchestration tools including Docker and Kubernetes.
• CI/CD pipelines for automated testing of non-deterministic AI outputs.
• Observability and logging pipelines for tracking agent token usage, latency, and failure states.
• Responsible AI frameworks, data privacy compliance, and bias mitigation guardrails.