Forward Deployed Engineer (FDE) — Washington, DC

Work Authorization: U.S. Citizenship Required

Position Overview

We are seeking an accomplished Forward Deployed Engineer (FDE) specializing in Data Engineering and Generative AI to design, develop, and deploy enterprise-grade software, data integration, and AI-powered solutions. The ideal candidate combines strong software engineering fundamentals, advanced data engineering capabilities, and practical experience implementing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI systems. This is a highly hands-on engineering position requiring the ability to translate complex business and operational challenges into scalable, secure, production-ready applications. The role involves full lifecycle technical ownership, including requirements discovery, solution architecture, rapid prototyping, software development, integration, deployment, performance optimization, and production support.

Successful candidates must demonstrate exceptional technical problem-solving abilities, architectural judgment, communication skills, and the ability to deliver results in dynamic environments with evolving requirements.

Key Responsibilities

• Solution Architecture & Technical Delivery

• Translate complex business and technical requirements into practical solution architectures and implementation plans.

• Lead hands-on development of scalable software, data engineering, and AI-driven solutions.

• Develop rapid prototypes and proof-of-concept applications to validate technical approaches.

• Own end-to-end technical delivery, including design, implementation, testing, deployment, and optimization.

• Evaluate architectural trade-offs involving scalability, performance, security, maintainability, and operational efficiency.

• Collaborate with cross-functional engineering, architecture, business, and operational teams.

• Software Engineering & API Development

• Design, develop, and maintain production-grade applications using Python and other modern programming languages.

• Build robust backend services, RESTful APIs, and enterprise application integrations.

• Develop reusable software components, integration services, and automation frameworks.

• Implement secure authentication, authorization, API integration, and error-handling mechanisms.

• Apply software development best practices, including version control, automated testing, CI/CD, documentation, and monitoring.

• Troubleshoot complex application, integration, infrastructure, and performance issues.

• Data Engineering & Enterprise Integration

• Design and implement scalable ETL/ELT pipelines for enterprise data ingestion, transformation, processing, and delivery.

• Develop data integration workflows connecting structured and unstructured data sources.

• Implement data modeling, schema mapping, validation, transformation, and reconciliation processes.

• Support batch and large-scale data processing using formats such as Parquet, CSV, and JSON.

• Develop integrations across relational databases, cloud storage, enterprise APIs, and modern data platforms.

• Optimize data processing pipelines for reliability, performance, accuracy, and scalability.

• Implement data quality monitoring, exception handling, and operational logging.

• Generative AI, LLMs & Agentic AI Engineering

• Design, develop, and deploy enterprise-grade Generative AI applications using Large Language Models.

• Build LLM-powered agents and agentic workflows to automate complex business and operational processes.

• Design and implement Retrieval-Augmented Generation (RAG) architectures integrating enterprise knowledge sources.

• Develop AI workflows incorporating tool/function calling, structured outputs, workflow orchestration, and multi-step reasoning.

• Integrate LLM applications with enterprise databases, APIs, business systems, and external tools.

• Implement vector embeddings, semantic retrieval, and vector database integrations.

• Develop evaluation frameworks to measure AI response accuracy, relevance, reliability, and overall application effectiveness.

• Implement appropriate human-in-the-loop controls, safeguards, feedback mechanisms, and monitoring.

• Optimize LLM-powered applications for latency, cost, scalability, security, and production performance.

• Cloud Engineering, DevOps & Production Deployment

• Design and deploy applications using cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.

• Support cloud-native development, deployment automation, and production infrastructure.

• Implement CI/CD pipelines, automated testing, logging, observability, and performance monitoring.

• Apply enterprise security, identity management, access control, and data governance standards.

• Troubleshoot production incidents and implement improvements to platform stability and reliability.

• Create reusable deployment patterns, technical documentation, and operational runbooks.

• Technical Leadership & Stakeholder Collaboration

• Lead technical discovery sessions and collaborate with engineering and business stakeholders.

• Communicate complex technical concepts clearly to both technical and nontechnical audiences.

• Independently evaluate ambiguous requirements and develop actionable engineering solutions.

• Identify technical risks, integration dependencies, data-quality concerns, and delivery constraints.

• Provide technical guidance, architectural recommendations, and engineering best practices.

• Contribute to reusable engineering frameworks, solution accelerators, and continuous improvement initiatives.

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, Data Engineering, or a related technical discipline, or equivalent practical experience.

• 8 14 years of professional experience in software engineering, data engineering, solution architecture, or related technical disciplines.

• Strong hands-on software engineering experience, particularly with Python.

• Proven experience designing and developing RESTful APIs, backend services, and enterprise integrations.

• Strong knowledge of ETL/ELT development, data pipelines, data modeling, schema mapping, and data transformation.

• Demonstrated experience building applications using Large Language Models (LLMs) and Generative AI technologies.

• Practical experience with one or more of the following:

• AI agents and agentic workflows

• Retrieval-Augmented Generation (RAG)

• LLM tool/function calling

• AI-powered workflow automation

• Enterprise LLM application development

• Experience integrating applications with enterprise data sources, databases, APIs, and cloud services.

• Strong debugging, troubleshooting, analytical, and software problem-solving capabilities.

• Experience delivering production-quality software and implementing sound engineering practices.

• Ability to independently design and deliver technical solutions from incomplete or evolving requirements.

• Strong interpersonal, written, and verbal communication skills.

• Experience interacting with technical leadership and cross-functional stakeholders.

• U.S. Citizenship is mandatory.

• Must be located in or able to work from the Washington, DC metropolitan area, with onsite presence 2 3 days per week.

Preferred Qualifications

• Previous experience as a Forward Deployed Engineer, Solutions Engineer, Solutions Architect, Technical Consultant, or customer-facing Software/Data Engineer.

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

• Hands-on experience with AWS, Azure, or Google Cloud Platform.

• Familiarity with modern data engineering technologies, including:

• Snowflake

• Databricks

• Apache Spark

• Apache Kafka

• Apache Airflow

• dbt

• Experience with AI application frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent technologies.

• Familiarity with vector databases, semantic search, embeddings, and retrieval architectures.

• Experience with TypeScript and modern application development frameworks.

• Experience deploying and monitoring Generative AI applications in production.

• Knowledge of AI evaluation frameworks, observability, and human-in-the-loop architectures.

• Understanding of enterprise cybersecurity, secure APIs, IAM, data privacy, and governance requirements.

• Experience building reusable engineering solutions and contributing to technical architecture decisions.

Core Technical Skills

Programming: Python, REST APIs, Backend Development, TypeScript

Data Engineering: ETL/ELT, Data Pipelines, Data Modeling, Schema Mapping, Data Integration, SQL, Parquet, CSV, JSON

Generative AI: LLMs, GenAI Applications, AI Agents, Agentic Workflows, RAG, Function Calling, Prompt Engineering, AI Workflow Automation

AI Frameworks: LangChain, LangGraph, LlamaIndex, Vector Databases, Embeddings

Cloud Technologies: AWS, Azure, GCP

Data Platforms: Snowflake, Databricks, Spark, Kafka, Airflow, dbt

Engineering Practices: CI/CD, Git, Automated Testing, API Security, Monitoring, Observability, Production Deployment

Ideal Candidate Profile

The ideal candidate is a highly capable, hands-on engineering professional who can independently architect, build, and deploy end-to-end data and Generative AI solutions.

Candidates should demonstrate a combination of strong Python software development, enterprise data engineering, practical LLM/agentic AI implementation, and production deployment experience.

This position is best suited for professionals who enjoy solving challenging technical problems, working across multiple technology domains, and taking direct ownership of engineering outcomes.

Location Requirement: Washington, DC Metro Area Hybrid (2 3 Days Onsite Weekly)

Citizenship Requirement: U.S. Citizens Only