Forward Deployed AI Engineer

Hybrid in Chicago, IL, US • Posted 1 day ago • Updated 1 day ago
Contract W2
12 Months
No Travel Required
On-site
Depends on Experience
Fitment

Dice Job Match Score™

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Job Details

Skills

  • Model Context Protocol
  • Agent-to-Agent
  • Vector databases
  • Python
  • AWS
  • Bedrock
  • Lambda
  • Docker
  • Kubernetes
  • AI frameworks
  • RAG
  • Retrieval-Augmented Generation
  • Multi-agent
  • Amazon SageMaker
  • Amazon S3
  • Artificial Intelligence
  • LangChain
  • PostgreSQL
  • Workflow
  • Microsoft Certified Professional
  • Cloud Computing
  • Regulatory Compliance
  • LangGraph
  • Data Extraction
  • Amazon RDS
  • Amazon Web Services
  • Collaboration
  • Leadership

Summary

Position: Forward Deployed AI Engineer

Location: Chicago, IL or Houston, TX

 

Job Description

Key Responsibilities

  • 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 Skill Required:

  • 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.
  • Multi-agent coordination patterns (supervisor-worker, decentralized collaboration, stateful graphs).
  • 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.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 90941404
  • Position Id: 9099759
  • Posted 1 day ago
Contact the job poster
KS

Kuldeep Singh

Recruiter @ Tekfortune Inc.
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