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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Forward Deployed Engineer/ Architect - Data Eng & GenAI (hybrid) in Washington - **Company:** Energy Jobline - **Location:** Washington, DC, United States - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Application Frameworks, Automated Storage and Retrieval Systems, User Authentication, Microsoft Azure, Databases, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Software Debugging, JSON, Python (Programming Language), Cloud Services, Software Engineering, Systems Integration, TypeScript, Enterprise Data Management, Parquet, Data Processing, Large Language Models, Snowflake, Apache Spark, Software Application Programming, Generative AI, Backend, Build Management, Apache Kafka, Restful APIs, Software Version Control, Data Pipelines, Api Management, Databricks - **Published:** September 6, 2026 - **Apply:** https://www.energyjobline.com/job/forward-deployed-engineer-architect-data-eng-genai-hybrid-washington-31539927 ## About the Role * 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. * U.S. Citizenship. * Based in or willing to work from the Washington, DC metro area. * Ability to work in a hybrid environment with office/customer-site presence at least 2-3 days per week. Strongly * 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, security, or other mission-critical environments. * Experience with cloud platforms such as AWS, Azure, or GCP. * 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. ## Description * Work directly with customers to understand business objectives, operational workflows, technical environments, and pain points. * Translate ambiguous customer requirements into concrete technical architectures and working solutions. * Rapidly prototype, build, test, deploy, and iterate on customer-facing solutions. * Own technical delivery from initial discovery through implementation and production adoption. * Make pragmatic engineering decisions balancing speed, scalability, security, maintainability, and customer impact. * Identify technical risks, data-quality issues, integration constraints, and implementation trade-offs early. * Write production-quality code, primarily using such as Python and/or TypeScript. * Design and develop APIs and backend services. * Integrate applications with databases, APIs, cloud services, AI models, and customer systems. * Build lightweight applications and interfaces where needed to deliver an end-to-end customer solution. * Apply sound software engineering practices around testing, version control, CI/CD, monitoring, security, and documentation. * Design and implement ETL/ELT pipelines for ingestion, extraction, transformation, and delivery workflows. * Execute bulk data processing and deliver data products in formats including Parquet, CSV, JSON, and related formats. * Build, configure, test, and maintain REST/API integrations for customer and internal use cases. * Design and build GenAI-powered applications that automate complex customer workflows. * Build LLM-based agents and agentic workflows capable of reasoning across enterprise data, APIs, and tools. * Develop RAG pipelines connecting LLMs with structured and unstructured enterprise data. * Implement tool/function calling, structured outputs, workflow orchestration, and multi-step AI systems. * Build evaluation frameworks and feedback loops to measure and improve AI application quality. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Parquet, Delta, Iceberg & Ducklake - An introduction for developers](https://www.wearedevelopers.com/videos/100075-parquet-delta-iceberg-ducklake-an-introduction-for-developers) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [Got AI ideas but no money? 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