Forward Deployed AI Engineer

Voto Consulting LLC; Chicago (IL); 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 with RAG pipelines and vector DB integrations. You will define multi-agent patterns, memory management, and autonomous planning, while ensuring privacy, compliance, and guardrails across AI touchpoints.

You will guide engineering teams, run stakeholder workshops, and craft reusable deployment patterns, leveraging modern tech stacks and cloud-native deployments on AWS.

Responsibilities

• Build enterprise Generative and Agentic AI platforms with high-performance RAG pipelines and vector database integrations.
• Define multi-agent collaboration patterns, memory management, and autonomous planning frameworks.
• Implement data privacy, compliance, risk mitigation, and evaluation guardrails across AI touchpoints.
• Guide and mentor engineering teams, run discovery workshops with stakeholders, and define reusable deployment patterns.
• Retrieval-Augmented Generation pipelines, semantic caching, and context window optimization.
• Function calling, tool use, and structured data extraction schemas.
• Evaluation metrics, tracing, and guardrails to reduce hallucinations.
• Design agentic-first workflows and autonomous decision loops.
• Frameworks like LangChain/LangGraph/Bedrock Core Runtime for state and memory management.
• Interoperability standards like MCP and A2A protocols.
• Vector databases (e.g., Milvus, Aurora PostgreSQL) for fast similarity search.
• Data pipelines and embedding generation workflows using Python, FastAPI, or Apache Spark.
• Cloud-native deployment on AWS services (Bedrock, Lambda, EKS, SageMaker, S3, RDS, DocumentDB).
• Containerization and orchestration with Docker and Kubernetes.
• CI/CD pipelines for testing non-deterministic AI outputs.
• Observability and logging for token usage, latency, and failures.
• Responsible AI, privacy compliance, and bias-mitigation guardrails.

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.