Description

AI / ML Engineer (WISRD Platform – ISR & Tactical Edge AI)

Location

Hybrid (Maryland Preferred)
Remote considered for highly qualified candidates

Clearance Requirement

Must be a U.S. Citizen and eligible to obtain a DoD Secret clearance

Company Overview

CyOne is a mission-focused software company delivering advanced capabilities to the U.S. Department of War and allied partners. Our flagship platform, WISRD (Wide-area ISR Discovery), enables multi-domain situational awareness, intelligence support to targeting, and ISR orchestration across cloud, on-premise, and tactical edge environments.

WISRD is actively deployed in operational environments and aligned with emerging Next Generation Command and Control (NGC2) initiatives. Our platform integrates and fuses data from ISR sensors, mission systems, and partner sources to support real-time decision-making in complex operational environments.

Position Overview

CyOne is seeking an AI / ML Engineer to lead the development of an AI-enabled assistant embedded within the WISRD platform. This capability will enable analysts and operators to query mission data, navigate workflows, generate insights, and trigger actions using natural language.

This is a high-impact, hands-on engineering role focused on building production AI systems that operate across enterprise and tactical edge environments, including disconnected, degraded, intermittent, and low-bandwidth (DDIL) conditions.

The selected candidate will serve as the technical lead for AI capabilities within WISRD, owning architecture, implementation, and delivery from concept through operational deployment.

Key Responsibilities

Design and build AI capabilities that enable users to query ISR data, generate insights, and trigger mission workflows through natural language
Develop and maintain Retrieval-Augmented Generation (RAG) pipelines grounded in structured and unstructured operational data
Implement and optimize vector search, embedding pipelines, and hybrid retrieval strategies to ensure accurate, reliable outputs
Build and maintain a provider-agnostic LLM integration layer supporting both enterprise AI services and self-hosted models
Design AI systems that operate effectively in DDIL (disconnected, degraded, intermittent, low-bandwidth) environments
Lead model selection, fine-tuning (LoRA/QLoRA), and evaluation using domain-specific datasets
Develop AI backend services and APIs, including orchestration, context management, and intent parsing
Integrate AI capabilities into the WISRD platform for real-time user interaction and workflow automation
Establish MLOps pipelines, monitor model performance (accuracy, latency, cost), and continuously improve system quality
Ensure AI solutions meet security, data handling, and multi-enclave operational requirements

Work Environment

Small, collaborative engineering team
Direct interaction with developers, DevOps engineers, and mission subject matter experts
Agile development environment with rapid iteration cycles
Opportunity to support real-world operational deployments

Compensation & Benefits

Competitive salary based on experience
Comprehensive benefits package (medical, dental, vision)
Paid time off and holidays
Opportunities for professional growth and advancement

Requirements

Technical Skills

Experience designing and implementing RAG pipelines in production
Experience with vector databases (e.g., pgvector, Qdrant, Pinecone, Weaviate)
Strong understanding of embeddings and semantic search
Experience fine-tuning LLMs (LoRA / QLoRA) using Hugging Face or similar
Proficiency in Python and modern backend frameworks (e.g., FastAPI)
Experience with PostgreSQL or similar relational databases
Experience with REST APIs and streaming technologies

Infrastructure & Tools

Experience with Docker and containerized deployments
Familiarity with Kubernetes and distributed systems
Experience with model serving frameworks (e.g., vLLM, TGI, Ollama)
Experience with GPU-based compute environments

Experience

3+ years of experience in AI/ML engineering or related field
Experience deploying AI/LLM-based systems to production
Experience working with structured or operational data
Ability to work independently in a fast-paced environment

Preferred Qualifications

Experience with hybrid search (vector + keyword retrieval)
Experience with model quantization techniques (GGUF, GPTQ, AWQ)
Experience deploying AI systems to edge or resource-constrained environments
Familiarity with Cloudera ML or similar enterprise platforms
Experience with ISR systems, geospatial data, or mission command environments
Background in DoD, intelligence community, or other regulated environments
Familiarity with Angular or TypeScript
Active DoD security clearance

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