<p>Senior AI Developer (Full Stack)</p> <p>Location, Charlotte, NC, USA<</p> <p>Job Title: Senior AI Developer (Full-Stack)<</p> <p>Senior, hands-on AI engineer to design, build, and productionize GenAI applications end-to-end. You'll lead the development of robust LangChain/LangGraph agentic workflows, high-quality RAG pipelines, and scalable microservices on Google Vertex AI. You'll own system design, implementation, MLOps, observability, and governance-partnering closely with product, data, security, and platform teams to deliver reliable, secure, and cost-efficient AI products.<</p> <p>Key Responsibilities< <</p> <p>Architecture & < Design multi-step agentic workflows with LangGraph (state machines, tools, retries, timeouts) and LangChain (chains, tools, memory).< Build guardrails (input/output filtering, red-teaming hooks) and observability (tracing, telemetry, logging, prompt/version tracking).< < < < < Own ingestion pipelines: chunking, embeddings, document normalization, metadata, and vector DB indexing (e.g., Pinecone, Weaviate, Milvus, FAISS).< Implement retrieval strategies: hybrid (BM25 + dense), multi-vector, reranking, query planning, LangGraph retrieval sub-graphs, caching.< Build domain-specific adapters (schema, ontology alignment) and grounding with structured tools/knowledge bases.< < < Vertex AI & < Productionize services on Google Vertex AI (Models, Endpoints, Workbench, Pipelines, Vector Search, Feature Store).< Containerize with Docker, orchestrate with Kubernetes/GKE, and automate with CI/CD (GitHub Actions/Cloud Build).< < < Full- < Build user-facing apps (React/Next.js) and backends (Python/FastAPI, Node/Express), including authentication/authorization and rate limiting.< Develop tooling/services (e.g., document loaders, evaluators, red-teaming flows, prompt versioning, synthetic data pipelines).</p> <p>Evaluation & Reliability</p> <ul> <li>Define and automate GenAI evaluation: relevance, faithfulness, hallucination rate, answer-exactness, latency, cost. </li><li>Use techniques like RAGAS, G-Eval, rubric-based human-in-the-loop, pairwise comparisons, A/B tests, and production feedback loops. </li></ul> <p>Security, Governance & Cost</p> <ul> <li>Implement data privacy controls (PII detection, masking), policy enforcement, prompt hardening, and audit logging. </li><li>Optimize latency and TCO (embedding/model selection, batching, caching, streaming, adaptive routing, quantization where applicable). </li></ul> <p>Mentorship & Standards</p> <ul> <li>Establish best practices for prompt patterns, orchestration, testing (unit & scenario), and model lifecycle management. </li><li>Mentor engineers; collaborate with product/design to scope features and deliver business impact. </li></ul> <p>Required Qualifications< < 7-10+ years software engineering experience; 3-5+ years applied ML/GenAI building production systems.< Expert with LangChain and LangGraph (tools, agents, state graphs, retries, sub-graphs, observability).< Hands-on with Vertex AI (Foundational models, Endpoints, Pipelines, Vector Search, Model Garden; IAM & service architectures).< Strong RAG practitioner (chunking strategies, embeddings, hybrid retrieval, rerankers like Cohere/Rerank or bge-rerank, evaluation).< Deep experience with vector databases (Pinecone, Weaviate, Milvus, FAISS) and embedding models (OpenAI, Vertex, Cohere, bge-large).< Production backends in Python (FastAPI) or Node.js, plus React/Next.js front-end experience.< Solid cloud experience (GCP preferred; AWS/Azure a plus), Docker/Kubernetes, and CI/CD.< Strong understanding of GenAI evaluation (RAGAS, G-Eval, rubric scoring), observability (LangSmith/LlamaIndex observability/OpenTelemetry), and prompt/version management.< Knowledge of security & governance: PII handling, isolation, data residency, prompt injection defenses, secret management.< Excellent communication; proven track record turning ambiguous problem statements into shipped products.< <</p> <p>Nice to Have< <</p> <p>Knowledge graphs (RDF/OWL), retrieval planning, and toolformer/agent patterns.< LLM serving and routing (DG/mixture-of-experts, function/tool calling, Guardrails, Instructor schemas, Pydantic).< LlamaIndex experience; structured RAG (SQL/Graph RAG); function/tool calling integrations (Databases, SaaS).< On-prem/vector-optimized deployments; GPU utilization, quantization, LoRA fine-tuning.< Experiment tracking (Weights & Biases), feature stores, offline/online evaluation pipelines.< Enterprise integrations (SharePoint, Confluence, Salesforce) and document governance.< New York10 - 12 Years10H09-Mar-2026YACTIVE117058-8-1</p>