Senior AI Developer (Full‑Stack)

Description

Title:Senior AI Developer (Full‑Stack)

Location: Charlotte, NC

Duration: 12 months

Work Engagement: W2

Work Schedule: Hybrid 3 days in office/2 days remote

Benefits on offer for this contract position: Health Insurance, Life insurance, 401K and Voluntary Benefits

Summary:

In this contingent resource assignment, you may: Consult on complex initiatives with broad impact and large-scale planning for Software Engineering. Review and analyze complex multi-faceted, larger scale or longer-term Software Engineering challenges that require in-depth evaluation of multiple factors including intangibles or unprecedented factors. Contribute to the resolution of complex and multi-faceted situations requiring solid understanding of the function, policies, procedures, and compliance requirements that meet deliverables. Strategically collaborate and consult with client personnel. Required Qualifications: Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education.

Key Responsibilities:

• Architecture & Orchestration

• 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).

• RAG Pipelines

• 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 & Platform Engineering

• 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‑Stack Delivery

• 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).

• Evaluation & Reliability

• Define and automate GenAI evaluation: relevance, faithfulness, hallucination rate, answer‑exactness, latency, cost.

• Use techniques like RAGAS, G‑Eval, rubric‑based human‑in‑the‑loop, pairwise comparisons, A/B tests, and production feedback loops.

• Security, Governance & Cost

• Implement data privacy controls (PII detection, masking), policy enforcement, prompt hardening, and audit logging.

Optimize latency and TCO (embedding/model selection, batching, caching, streaming, adaptive routing, quantization where applicable).

• Mentorship & Standards

• Establish best practices for prompt patterns, orchestration, testing (unit & scenario), and model lifecycle management.

Mentor engineers; collaborate with product/design to scope features and deliver business impact.

Key Requirements:

• Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.

• 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.