AI/ML Engineer - ACG / AEA

Remote

Contingent Contract Award

6 month opportunity 

Springfield, VA

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.

Key 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.

• 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.

Must-Have Skill Sets (Technical + Methodologies)

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

Total Rewards Statement:

We believe in fairness and clarity throughout our hiring process. The anticipated salary range for this position is $155,000.00 to $165,000.00 good faith range based on factors such as your experience, geographic location, and any applicable contractual requirements, and may vary slightly.

Beyond salary, we provide a robust benefits package and encourage ongoing professional development, because your growth and well-being matter to us. We’re excited to support you in building a rewarding career with us!

Connected Logistics respects the need for confidentiality for all applicants.

Connected Logistics offers an excellent benefits package that includes health, dental, vision, life, and disability insurance, a great 401(k) package, and generous Paid Time Off.

EOE/Disability/Veterans