Applied AI Tech Lead - ACG / AEA

Connected Logistics•Springfield, VA

3d•$160,000 - $170,000•Remote

About The Position

Connected Logistics is seeking an Applied AI Tech Lead to serve as the technical authority for AI/ML architecture, integration, and governance within a controlled DevSecOps environment. The role involves owning the design and implementation of retrieval-augmented generation (RAG), model lifecycle management, and the secure integration of AI capabilities into enterprise workflows and pipelines.

Requirements

Minimum 10 years’ experience in AI/ML engineering, data science, or distributed systems development.
Master’s degree required (no exceptions) in Computer Science, Engineering, Mathematics, or related field.
Must have an Active Public Trust clearance or higher.
Must have been issued a CAC by another government client in the last 24 months.
Deep experience with RAG architectures (embeddings, vector DBs, retrieval optimization).
Strong Python proficiency and experience building production ML services and APIs.
Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and LLM integration patterns.
Hands-on experience with cloud-native architectures (AWS and/or Azure)
Experience integrating AI components into CI/CD pipelines (build, test, deploy).
Experience working in regulated or secured environments with audit and compliance requirements.
RAG Architecture (hands-on experience)
Embeddings (OpenAI, HF, or similar)
Vector databases (Pinecone, OpenSearch, FAISS, or equivalent)
Retrieval tuning (top-k, re-ranking, grounding strategies)
LLM Integration Patterns
Prompt engineering with versioning/control
Context window optimization
Guardrails and response validation
Model Lifecycle Management
Model versioning and registry concepts
Evaluation frameworks (precision/recall, relevance scoring)
Drift detection and performance monitoring
Cloud-Native Architecture (must be hands-on)
AWS (Lambda, S3, Bedrock/SageMaker) and/or Azure (ML, Functions, Storage)
Secure service-to-service integration patterns
API-first design
DevSecOps Integration
CI/CD pipelines (Azure DevOps, Git-based workflows)
Automated testing for ML systems
Deployment strategies (blue/green, rollback)
Data + ML Pipeline Integration
End-to-end flow: ingestion to transformation to embedding to retrieval to inference.
Handling structured + unstructured data in production systems.
Security & Governance Implementation
PII/CUI handling-in pipelines
Audit logging and traceability design
Access control patterns for ML systems
System Design for Enterprise Workflows
Event-driven and microservices architecture
Integration into existing systems (e.g., Salesforce, ticketing systems)
High - availability / low - latency design

Responsibilities

Architect end-to-end AI/ML solutions, including RAG pipelines, embedding strategies, vector indexing, and inference workflows.
Define and implement model lifecycle controls: versioning, evaluation, audit logging, traceability, and rollback.
Design secure integration patterns across AWS, Azure, Salesforce, and Azure DevOps.
Establish governance aligned to RMF constraints, including PII/CUI handling, prompt control, and usage auditing.
Define model evaluation frameworks (precision/recall, relevance scoring, latency, SLA adherence).
Implement monitoring for model performance, drift detection, and data quality degradation.
Oversee CI/CD integration for model deployment, retraining, and rollback.
Lead root-cause analysis and triage automation architecture (classification, similarity search, SLA prediction).
Review and enforce coding standards for ML pipelines, APIs, and data flows.
Mentor engineers and direct technical execution across data, model, and integration layers.