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Forward Deployed Engineer Data Engineering & GenAI

RapidIT, IncWashington, DC🇺🇸United StatesPosted Sep 30, 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Washington, DC, United States
Posted
Yesterday
AWSETLSnowflakeAirflowAzureDatabricksGenerative AIGoogle CloudKafkaLLMPythonRESTdbt

Job Description

Job Title: Forward Deployed Engineer Data Engineering & GenAI

Location: Washington, DC (Hybrid expected in the office/customer sites 2 3 days per week)

Duration: 12+ months

Must Have

Strong hands-on software engineering experience.

Strong data engineering fundamentals, including ETL/ELT, data modeling, schema mapping, and data pipelines.

Experience designing and integrating REST APIs and backend services.

Strong Python development skills.

Hands-on experience building applications using LLMs / Generative AI.

Experience building at least some of: AI agents, RAG systems, tool-calling workflows, LLM-powered applications, or AI workflow automation.

Ability to take an ambiguous customer requirement and independently turn it into a working technical solution.

Strong debugging and problem-solving skills across applications, APIs, infrastructure, and data.

Excellent written and verbal communication skills.

Demonstrated ability to work directly with customers and senior stakeholders.

Ability to operate effectively in fast-moving environments with incomplete requirements.

Ability to work in a hybrid environment with office/customer-site presence at least 2 3 days per week.

Strongly Preferred

Experience working as a Forward Deployed Engineer, Solutions Engineer, Solutions Architect, Technical Consultant, or customer-facing Software/Data Engineer.

Experience supporting the federal government, defense, intelligence, national security, or other mission-critical environments.

Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.

Experience with modern data platforms and technologies such as Snowflake, Databricks, Spark, Kafka, Airflow, dbt, or equivalent technologies.

Experience with vector databases, embeddings, retrieval systems, and modern LLM application frameworks.

Experience deploying AI applications into production environments.

Experience designing human-in-the-loop workflows and AI evaluation systems.

Familiarity with enterprise security, authentication, authorization, and data-governance requirements.

Demonstrated ability to develop reusable technical approaches and influence engineering or product strategy.

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