Forward Deployed Engineer at Oran Inc

Oran Inc; Herndon (VA); Hybrid; USD 170,000 - 210,000; Full time

Job summary

Oran Inc. is seeking a Senior AI/ML Engineer (Forward Deployed) to lead AI initiatives for Federal Agencies.

Based in Washington, DC, this hybrid role requires hands-on architectural, engineering, and operational leadership across discovery, design, development, testing, and deployment of AI-enabled systems. The candidate will guide data flows, security, MLOps, evaluation, and governance while coordinating with CIO/OCIO and enterprise teams.

Qualifications

• Bachelor’s degree or higher in a relevant field; Master’s preferred.
• Seven+ years of hands-on AI/ML, software, cloud, or data engineering experience.
• Production experience with generative AI, LLMs, RAG, or AI system operations.
• Experience delivering across AI, app dev, data eng, UX, integration, and ops.

Responsibilities

• Lead agency-specific AI discovery, architecture, deployment, and support.
• Translate mission needs into measurable problem statements and backlogs.
• Design data flows, access controls, trust boundaries, and observability.
• Direct MLOps/LLMOps practices including CI/CD and model lifecycle.
• Coordinate with federal stakeholders and ensure compliance artifacts.

Skills

AI/ML engineering

Delivery leadership

Stakeholder advisory

Cloud engineering

Security & compliance

Education

Bachelor’s degree in CS or related

Master’s degree preferred

Tools

Azure

AWS

Google Cloud

Databricks

Vector databases

Job description

Senior AI/ML Engineer - Forward Deployed Engineer

This Full time on site position offers great opportunities for career growth.

Work Location: Washington, DC - Hybrid; at least three days onsite per week

Position Summary

The Senior AI/ML Engineer serves as a senior Forward Deployed Engineer and accountable technical lead supporting one or more Federal Agencies. The role translates ambiguous mission needs into secure, usable, accessible, and Government-owned AI capabilities. The incumbent provides hands‑on architecture, engineering, AI assurance, delivery leadership, and stakeholder advisory support across discovery, design, development, testing, deployment, operations, and knowledge transfer.

Essential Responsibilities

• Lead agency-specific AI solution discovery, feasibility analysis, architecture, engineering design, integration, deployment, and operational support.
• Translate incomplete mission needs into measurable problem statements, user needs, acceptance criteria, prioritized backlogs, experiments, and release plans.
• Architect and implement AI-enabled applications using appropriate methods, including classical ML, document intelligence, NLP, LLM integrations, RAG, agentic workflows, multi-agent patterns, MCP server-based components, APIs, and deterministic automation.
• Select the simplest safe and effective solution rather than defaulting to generative AI.
• Lead the technical design of data flows, identity and access controls, trust boundaries, fallback and rollback methods, observability, and resilience.
• Direct MLOps/LLMOps practices, including version control, CI/CD, model/artifact registration, evaluation, monitoring, retraining or refresh workflows, and release management.
• Define and oversee AI evaluation methods for accuracy, groundedness, relevance, hallucination, calibration, fairness, robustness, explainability, and operational impact.
• Lead AI‑specific adversarial testing for prompt injection, jailbreaks, data poisoning, retrieval manipulation, insecure tool use, agent-loop failures, secrets exposure, model extraction, and vector-store corruption.
• Ensure human-in-the-loop controls, documented override and escalation mechanisms, audit logging, and clear distinction between AI-generated and human-verified outputs.
• Produce or direct production-ready architecture artifacts, technical documentation, model cards, test evidence, SBOM inputs, operational runbooks, and release packages.
• Coordinate directly with Federal Agency CIO/OCIO organizations, AI governance teams, enterprise platform teams, cloud organizations, architecture review boards, security authorization teams, privacy, accessibility, legal, records, and other Government stakeholders.
• Support RAIA, PIA/PTA, ATO/RMF, SSP, SAP, SAR, POA&M, and related compliance documentation.
• Ensure custom code, prompts, configurations, models, datasets, embeddings, and deployment assets are developed and maintained in Government-controlled repositories.
• Serve as the technical primary point of contact for assigned Federal Agency stakeholders and provide planned/unplanned coverage for other key personnel.
• Act as, or work directly with, the designated Security Lead to certify security evidence for each applicable deliverable.

Minimum Qualifications

• Bachelor’s degree in computer science, data science, software engineering, information systems, statistics, engineering, or comparable field.
• Master’s degree preferred.
• Seven or more years of hands‑on experience in AI/ML engineering, software engineering, cloud engineering, enterprise architecture, or data engineering, including delivery of AI-enabled systems.
• At least two years of hands‑on production experience in one or more of generative AI, LLM integration, RAG, model evaluation, AI agents, or production AI system operations.
• Demonstrated ability to deliver across AI, application development, data engineering, UX, integration, and operations in ambiguous environments.
• Demonstrated experience designing secure enterprise or Federal AI architectures, including cloud AI platforms, APIs, data pipelines, vector databases, model orchestration, MCP, evaluation systems, and MLOps.
• Working knowledge of Federal or comparably regulated security, privacy, accessibility, Responsible AI, ATO/RMF, FISMA, FedRAMP, and PII/CUI protection practices.
• Demonstrated experience advising Government or executive stakeholders and leading technical delivery across multiple teams.

Required Certification Evidence

• Azure AI / Generative AI certification
• AWS Generative AI or Machine Learning certification
• Google Cloud Generative AI / ML credential
• Databricks Generative AI Engineer credential
• NVIDIA Generative AI credential
• Certified Artificial Intelligence Professional (CAIP)
• Equivalent recognized AI/ML certification

Certification evidence must be supplied at onboarding and annually thereafter. Self‑attestation is not sufficient.

Preferred Qualifications

• Direct experience supporting Federal Agencies, especially systems involving PII, CUI, public-facing digital services, grants, enforcement, benefits, claims, or case-management operations.
• Experience with AWS, Azure AI Foundry, Google Vertex AI, AWS Bedrock, GitHub Copilot/VS Code, GitLab, JIRA, and Government-controlled CI/CD pipelines.
• Experience with accessibility-by-design and delivery of systems meeting Section 508/WCAG 2.1 A/AA.
• Experience supporting independent testing, red teaming, security assessment, and production ATO activities.