AI/ML Engineer - ACG / AEA

Connected Logistics•Springfield, VA

3d•$155,000 - $165,000•Remote

About The Position

Connected Logistics is seeking an AI/ML Engineer to build and integrate machine learning components into enterprise workflows. The focus is on implementing models, RAG pipelines, and supporting services that enable automation, classification, retrieval, and intelligent decision support. Ideally, the engineer should have prior successes building and deploying RAG + ML services in production. This opportunity is expected to last for six (6) months with travel to Washington, DC.

Requirements

Minimum 10 years of experience in AI/ML engineering, software development, or data science.
Master’s degree required in Computer Science, Engineering, or related field.
Must have an Active Public Trust clearance or higher.
Strong experience with Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
Experience with embeddings, vector similarity search, and retrieval systems.
Experience building and deploying APIs or microservices for ML inference.
Hands-on experience with AWS and/or Azure environments.
Experience integrating into CI/CD pipelines and production systems.
RAG Implementation (hands-on build experience)
Document ingestion + chunking strategies
Embedding generation and storage
Vector similarity search and retrieval optimization
Machine Learning Model Development
Classification, clustering, and ranking models
Feature engineering and dataset preparation
Model tuning and evaluation
LLM Application Development
Prompt construction and chaining
Output validation and structured responses
Integration of LLMs into workflows (not just experimentation)
API and Service Development
RESTful API design and implementation
Serving ML models in production (FastAPI, Flask, etc.)
Stateless service design
CI/CD for ML Systems
Model deployment pipelines
Automated testing and validation before release
Version control for code + models
Cloud Deployment
Running ML workloads in AWS or Azure
Containerization (Docker)
Basic orchestration patterns (serverless or container-based)
Search and Similarity Systems
Embeddings + cosine similarity / ANN search
Duplicate detection patterns
Ranking and scoring logic
Performance Optimization
Latency reduction for inference
Efficient batching / caching strategies
Memory and compute-tuning

Responsibilities

Develop ML models and supporting services for classification, clustering, similarity search, and prediction.
Implement RAG pipelines: document ingestion, embedding generation, vector indexing, and retrieval tuning.
Build APIs and microservices to expose model capabilities to enterprise systems.
Integrate ML components into existing DevSecOps pipelines (Azure DevOps, CI/CD workflows).
Implement duplicate detection, ticket routing, SLA prediction, and root-cause assist features.
Optimize model performance for latency, throughput, and accuracy.
Conduct model evaluation, error analysis, and iterative tuning.
Work with Data Engineer to align data pipelines with model input requirements.
Ensure outputs are explainable, auditable, and compliant with governance controls.