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Forward Deployed AI Engineer

Tekfortune Inc.Chicago, IL🇺🇸United StatesPosted Sep 30, 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Chicago, IL, United States
Posted
20 hours ago
DockerFastAPIAWSApacheApache SparkKubernetesPostgreSQLPython

Job Description

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.

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