[Remote] AI/ML Engineer - ACG / AEA

Note: The job is a remote job and is open to candidates in USA. Connected Logistics is seeking an AI/ML Engineer to build and integrate machine learning components into enterprise workflows. The role focuses on implementing models, RAG pipelines, and supporting services that enable automation and intelligent decision support.

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

Skills

• 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

Benefits

• Health, dental, vision, life, and disability insurance
• A great 401(k) package
• Generous Paid Time Off
• Ongoing professional development

Company Overview