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
- 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; 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).
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