Senior AI Developer (Full-Stack)

Outstanding long-term contract opportunity! A well-known Financial Services Company is looking for a Software Engineer in Charlotte, NC (Hybrid).

Work with the brightest minds at one of the largest financial institutions in the world. This is a long-term contract opportunity that includes a competitive benefit package! Our client has been around for over 150 years and is continuously innovating in today's digital age. If you want to work for a company that is not only a household name, but also truly cares about satisfying customers' financial needs and helping people succeed financially, apply today.

Contract Duration: 12 Months

Required Skills & Experience

• 5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education.
• 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 (Google Cloud Platform 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.

Desired Skills & Experience

• 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).

What You Will Be Doing

• 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.
• 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.