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Salesforce

Builds cloud software for customer relationships

Lead Forward Deployed Engineering - Data Science & Integration

AI Application Engineering +1Confirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 8 years of experience in a hands-on, end-to-end delivery role for high-quality, scalable production solutions, ideally in customer-facing or field-deployed settings.
  • A strong background in Computer Science, Data Science, or a related engineering discipline.
  • Expert-level proficiency in one or more programming languages, such as Python, Java, or SQL.
  • Extensive hands-on experience building and deploying AI/ML solutions, including model development, MLOps pipelines, LLM integration, prompt engineering, and agentic frameworks such as LangChain and LlamaIndex.
  • Demonstrated expertise in enterprise integration platforms and API-led connectivity design, using tools such as MuleSoft Anypoint Platform, Dell Boomi, Apigee, Azure API Management, or equivalent platforms.
  • Demonstrated expertise in workflow orchestration and process automation platforms, using tools such as Regrello, Temporal, Prefect, Apache Airflow, or equivalent platforms.
  • Deep expertise in data modeling, processing, and analytics, with proficiency in enterprise data platforms such as Snowflake, Databricks, BigQuery, or Redshift.
  • Ability to deliver effectively in ambiguous, customer-site environments.
  • Ability to communicate and present complex data science and integration concepts to technical and non-technical mission stakeholders.
  • Ability to lead technical engagements, mentor junior FDEs, and own technical delivery end-to-end.
  • Ability to travel 50–75% of the time to customer sites, including classified facilities.
  • Must be a U.S. citizen, U.S.-born or naturalized, living on U.S. soil, and must not hold dual citizenship.
  • Must be eligible and willing to obtain a U.S. security clearance.
  • Must agree to complete a U.S. federal government Minimum Background Investigation for a Moderate Public Trust position.

Responsibilities

  • Serve as the forward-deployed technical lead on customer engagements, designing, building, and deploying tailored data science and integration solutions for defense, intelligence, and aerospace missions on-site.
  • Architect and implement enterprise integration solutions using MuleSoft Anypoint Platform or equivalent iPaaS tooling, including API design, development, and management across REST, SOAP, and event-driven integration patterns within secure customer environments.
  • Design and deploy workflow automation solutions using Regrello or equivalent orchestration tooling to model complex operational processes, coordinate cross-functional teams, and surface data-driven insights at mission decision points.
  • Lead integration of AI/ML models, custom data pipelines, and automation flows supporting predictive maintenance, personnel planning, and mission-critical logistics decisions.
  • Work with customer IT and security teams to accelerate Authority to Operate (ATO) and production go-lives in accordance with DoD IL5/IL6 and applicable regulatory frameworks.
  • Partner with internal product and engineering teams to co-develop integration patterns and data science capabilities informed by frontline customer feedback.
  • Enable customers to adopt modern data platforms and integration middleware for field operations, asset visibility, command center analytics, and secure operational workflows.
  • Mentor junior FDEs and share lessons with the broader engineering community to shape playbooks, integration templates, and reusable data science assets for government innovation.
  • Translate mission priorities into actionable data and integration roadmaps aligned with agency strategy, presenting findings and recommendations to senior stakeholders.
  • Take end-to-end technical responsibility for complex data pipelines, integration flows, and predictive models by writing code, configuring systems, troubleshooting in the field, and guiding junior team members.
  • Design, develop, and implement scalable production machine-learning and analytics systems, including predictive models, intelligent automation pipelines, and LLM-augmented workflows.
  • Design and build custom API-led integrations using enterprise integration platforms or equivalent middleware to connect enterprise data sources, operational systems, and cloud environments in regulated settings.
  • Model, automate, and optimize multi-stakeholder operational processes in manufacturing, logistics, supply chain, and mission operations contexts using workflow automation and process orchestration platforms.
  • Own the data lifecycle on-site with customers, including strategic data modeling, ETL/ELT pipeline construction, feature engineering, and integration strategy.
  • Lead rapid prototyping and iteration by developing proofs of concept and MVPs alongside customer teams.

Desired Qualifications

  • Prior experience in a forward-deployed, field engineer, or embedded technical advisor role with direct customer engagement.
  • Experience supporting U.S. Department of Defense (DoD), Intelligence Community (IC), or Federal Civilian agencies.
  • Background in defense, aerospace, or military logistics operations.
  • Familiarity with IL5/IL6/CNSS #1253 requirements or deployment to classified environments.
  • Hands-on experience with MuleSoft Anypoint Platform, including advanced DataWeave and Runtime Fabric deployments.
  • Hands-on experience with Regrello for operational workflow orchestration in manufacturing, supply chain, or logistics domains.
  • Expert-level Python and/or Java proficiency in a professional data science or integration engineering setting.
  • Experience developing and deploying LLM-augmented solutions in highly regulated or classified industries.
  • Relevant platform certifications, such as MuleSoft Certified Developer or Architect, Databricks Certified Associate or Professional, AWS/GCP/Azure Data or ML Engineer certifications, or data science and analysis certifications.
  • Experience with event-driven architectures, messaging systems such as Kafka or RabbitMQ, and streaming data pipelines.
  • Familiarity with CI/CD pipelines, containerization using Docker or Kubernetes, and DevSecOps practices in regulated environments.

About the Company

Salesforce

Builds cloud software for customer relationships

Salesforce sells cloud software that helps businesses manage sales leads, customer service, marketing, and online commerce from shared customer records. Its flagship Sales Cloud tracks accounts and deals, while Service Cloud organizes support cases and Agentforce builds AI assistants that act on company data. Teams access these tools through a browser and connect outside apps and data sources, with Slack for work messages and Tableau for visual analysis. Salesforce earns most of its revenue from software subscriptions and support, with professional services for implementation and training. Marc Benioff co-founded Salesforce in 1999, and its headquarters are in San Francisco, California.

Company size
10,001+
Company stage
IPO
Headquarters
San Francisco, CA
Founded
1999

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