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