Forward Deployed Engineer/ Architect - Data Eng & GenAI (hybrid)

Job Description

Job Description

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.
• 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 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 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.

 

Roles & Responsibilities

• 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 languages 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.