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# Froward Deployment Engineer

- **Company:** Intersources Inc.
- **Location:** Charlotte, NC, United States
- **Experience:** Expert
- **Salary:** $26,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Information Engineering, Python (Programming Language), Machine Learning, Software Deployment, Software Engineering, Data Streaming, Unstructured Data, Data Logging, Cloud Platform System, Large Language Models, Multi-Agent Systems, Generative AI, Kubernetes, Optimization Algorithms, Machine Learning Operations, Virtual Agents, Data Pipelines, Docker, Microservices
- **Published:** September 15, 2026
- **Apply:** https://www2.jobdiva.com/portal/?a=62jdnw10t7d77ytz0lbaey9qxonk3b05a9tqw96rb7z6hlfo7xj79l9g6mp6aj2o&compid=0/jobs/33091820#/jobs/33091820

## About the Role

1. Core Software Engineering & Python
 * Expertise in Python: Deep proficiency in building production-grade applications, APIs, and microservices using clean, maintainable Python syntax.
 * Data & Cloud Infrastructure: Practical experience building containerized applications using Docker/Kubernetes and working with major cloud ecosystems (AWS, GCP, or Azure).
 * Data Engineering: Experience designing large-scale pipelines using vector databases and event-driven data streaming layers.

 2. Advanced Generative AI & Agentic AI
 * LLM Orchestration: Mastery of foundational GenAI frameworks such as LangChain, LangGraph, LlamaIndex, or CrewAI.
 * RAG & Context Management: Proven success implementing semantic search, custom context windows, embeddings, and prompt optimization techniques.
 * Agentic Frameworks: Hands-on experience developing autonomous AI agents with bounded tool use, self-reflection, and structured output patterns.

 3. Classical AI/ML Background
 * Machine Learning Fundamentals: Deep understanding of classical ML models for forecasting, predictive analytics, anomaly detection, and classification.
 * MLOps & LLMOps: Familiarity with the model lifecycle, utilizing instrumentation tools for logging, tracing, evaluation, cost tracking, and model observation (e.g., MLflow, Kubeflow, Weights & Biases).

## Description

We are seeking a highly skilled and client-facing Forward Deployed AI Engineer with a powerful mix of Python development, classical AI/ML expertise, and advanced Generative AI engineering capabilities.
In this role, you will bridge the gap between our cutting-edge AI technologies and enterprise customer deployment. You will build, adapt, and deploy production-grade AI solutions directly within client environments, turning ambiguous business problems into highly scalable, intelligent systems. As a Forward Deployed Engineer, you act simultaneously as a product engineer, an AI specialist, and a solution architect.
Key Responsibilities

 * End-to-End AI Deployment: Architect, deploy, and scale high-performance GenAI and AI/ML solutions into client enterprise environments-from initial technical discovery to final delivery.
 * Prototype to Production: Rapidly design and build functional Proof of Concepts (PoCs) and live interactive demos under time-sensitive customer engagements.
 * Pipeline & Agent Engineering: Construct resilient data processing pipelines handling both structured and unstructured data, using Retrieval-Augmented Generation (RAG) and multi-agent system workflows.
 * Cross-Functional Collaboration: Partner directly with the core engineering and product teams to translate field insights, edge-case bugs, and customer feedback into product roadmaps.
 * Client Advisory & Trust: Serve as the technical point of contact for customer stakeholders, explaining complex LLM behaviors, inference constraints, and engineering tradeoffs clearly.