Job Description
Role: AI Engineer
Location: Remote (NJ)
Type: Contract
- We are seeking an experienced AI Engineer / Diamond Layer Tech Lead to lead the design and development of advanced AI agent platforms, semantic retrieval solutions, knowledge graphs, and data foundations. This role requires a hands-on technical leader with strong expertise in Python, Agentic AI, RAG, knowledge graphs, GCP, BigQuery, and AI evaluation/observability.
- The Tech Lead will own the orchestration, semantic, and data-foundation architecture while developing reusable agent patterns, mentoring engineers, and driving solutions toward production readiness.
Key Responsibilities:
AI Agent & Backend Engineering
- Design and develop advanced AI agent services using Python.
- Build multi-step agent workflows, orchestration state management, and tool invocation frameworks.
- Develop reusable agent patterns, fallback mechanisms, and multi-agent execution workflows.
- Implement deterministic and LLM-assisted routing across multiple domains.
- Design standard contracts for agents, tools, and model/platform adapters.
- Develop agent registration and multi-domain execution capabilities.
RAG & AI Retrieval
- Design and implement advanced Retrieval-Augmented Generation (RAG) solutions.
- Develop retrieval, reranking, grounding, and context-assembly strategies.
- Improve answer accuracy and reduce hallucinations through effective retrieval and grounding techniques.
- Support citation, source attribution, and cross-domain synthesis.
- Design retrieval solutions that provide reliable and traceable answers from enterprise data.
Knowledge Graph & Semantic Engineering
- Design and develop enterprise knowledge-graph solutions using:
- RDF
- SPARQL
- Ontology Modeling
- SHACL
- Stardog
- Virtual Graphs
- Develop relational-to-semantic data mappings.
- Manage semantic versioning and graph promotion processes.
- Establish semantic models that support AI agents, retrieval, and cross-domain data discovery.
- 4. Data Engineering & Data Foundations
- Work extensively with SQL and Google BigQuery.
- Perform source discovery and data-gap analysis.
- Develop and maintain data dictionaries and business-key validation processes.
- Perform data reconciliation and source-of-truth assessments.
- Evaluate data quality, freshness, lineage, and completeness.
- Build reliable data foundations supporting AI agents and semantic platforms.
- 5. Cloud & Infrastructure
- Design and deploy AI agent services within Google Cloud Platform (GCP).
- Work with:
- GKE
- BigQuery
- Google Cloud Storage (GCS)
- Support agent-runtime deployment and semantic-platform connectivity.
- Configure secure service identities and environment-specific deployments.
- Ensure scalable and secure infrastructure for production AI workloads.
- 6. CI/CD & Release Engineering
- Establish CI/CD pipelines for AI agents and knowledge-graph components.
- Implement automated evaluation gates as part of the release process.
- Manage versioning for:
- Prompts
- Tools
- Data mappings
- Ontologies
- Queries
- Deployment configurations
- Promote tested AI and semantic components across environments.
- 7. AI Testing & Evaluation
- Establish evaluation frameworks for AI agents based on:
- Accuracy
- Relevance
- Groundedness
- Completeness
- Hallucination rate
- Latency
- Cost
- Develop benchmark scenarios and gold-answer datasets.
- Implement automated evaluation processes to validate agent quality before production releases.
- 8. Observability & Operations
- Implement end-to-end tracing across:
- Master coordinator
- Domain agents
- Tools
- Semantic layer
- Data layer
- Use Langfuse or equivalent LLM observability platforms.
- Develop operational dashboards and reporting using Grafana.
- Monitor model performance, agent behavior, latency, usage, and cost.
- Support model comparison and continuous optimization.
- 9. Security & Governance
- Implement identity-aware retrieval and least-privilege data access.
- Establish cross-domain security guardrails.
- Protect prompts, traces, and sensitive data throughout the agent lifecycle.
- Ensure source attribution and complete auditability of AI-generated responses and agent activities.
- Support secure and governed access to enterprise data and semantic resources.
- Tech Lead Responsibilities
- Own the technical design for AI orchestration, semantic architecture, and data foundations.
- Remain hands-on with architecture, coding, integration, and troubleshooting.
- Define reusable patterns and engineering standards for AI agents and semantic solutions.
- Mentor engineers and provide technical guidance across the development team.
- Drive technical decisions related to agent architecture, retrieval, knowledge graphs, and data platforms.
- Establish engineering practices for evaluation, observability, security, and production readiness.
- Collaborate with engineering and business stakeholders to translate requirements into scalable AI solutions.
- Lead solutions from architecture and development through testing, deployment, and production support.
Job Tags
Contract work, Remote work