Note: The job is a remote job and is open to candidates in USA. Connected Logistics is seeking an Applied AI Tech Lead to be the technical authority over AI/ML architecture, integration, and governance within a controlled DevSecOps environment. The AI Tech Lead will own design and implementation of retrieval-augmented generation(RAG), model lifecycle management, and secure integration of AI capabilities into enterprise workflows and pipelines.
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
Skills
- 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
Benefits
- Health, dental, vision, life, and disability insurance
- A great 401(k) package
- Generous Paid Time Off
Company Overview