Fullstack Developer (contract)

Description

Title : Senior AI Developer (Full Stack)

Location : Charlotte, NC

Duration : 9 months

Work Engagement : W2

Work Schedule : Onsite Full-Time

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
o Design multi step agentic workflows with LangGraph (state machines, tools, retries, timeouts) and LangChain (chains, tools, memory).
o Build guardrails (input/output filtering, red teaming hooks) and observability (tracing, telemetry, logging, prompt/version tracking).

• RAG Pipelines

o Own ingestion pipelines: chunking, embeddings, document normalization, metadata, and vector DB indexing (e.g., Pinecone, Weaviate, Milvus, FAISS).
o Implement retrieval strategies: hybrid (BM25 + dense), multi vector, reranking, query planning, LangGraph retrieval sub graphs, caching.
o Build domain specific adapters (schema, ontology alignment) and grounding with structured tools/knowledge bases.

• Vertex AI & Platform Engineering

o Productionize services on Google Vertex AI (Models, Endpoints, Workbench, Pipelines, Vector Search, Feature Store).
o Containerize with Docker, orchestrate with Kubernetes/GKE, and automate with CI/CD (GitHub Actions/Cloud Build).

• Full Stack Delivery

o Build user facing apps (React/Next.js) and backends (Python/FastAPI, Node/Express), including authentication/authorization and rate limiting.
o Develop tooling/services (e.g., document loaders, evaluators, red teaming flows, prompt versioning, synthetic data pipelines).

• Evaluation & Reliability

o Define and automate GenAI evaluation: relevance, faithfulness, hallucination rate, answer exactness, latency, cost.
o Use techniques like RAGAS, G Eval, rubric based human in the loop, pairwise comparisons, A/B tests, and production feedback loops.

• Security, Governance & Cost

o Implement data privacy controls (PII detection, masking), policy enforcement, prompt hardening, and audit logging.
o Optimize latency and TCO (embedding/model selection, batching, caching, streaming, adaptive routing, quantization where applicable).

• Mentorship & Standards

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