Senior AI Developer
Strategic Staffing Solutions
Job Description Senior AI Developer
Location: Charlotte, NC
Hybrid: 3 days onsite / 2 days remote
Shift: Monday through Friday 8am - 5pm
Pay Rate: Required Qualifications 7-10+ years software engineering experience; 3-5+ years applied ML/GenAI building production systems.
Expert with Lang Chain and Lang Graph (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 (Fast API) 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/Open Telemetry), 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.
Nice to Have
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).
Location: Charlotte, NC
Hybrid: 3 days onsite / 2 days remote
Shift: Monday through Friday 8am - 5pm
Pay Rate: Required Qualifications 7-10+ years software engineering experience; 3-5+ years applied ML/GenAI building production systems.
Expert with Lang Chain and Lang Graph (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 (Fast API) 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/Open Telemetry), 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.
Nice to Have
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).
Vacancy posted 3 days ago
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