What are the responsibilities and job description for the Generative AI Engineer position at Wise Equation Solutions Inc.?
Senior AI Developer (Full Stack)
Charlotte, NC (Onsite)
Contract (W2 Only)
Job Description
Must have: LangChain, LangGraph, Agentic AI development, MLOPS
Senior, hands on AI engineer to design, build, and productionize GenAI applications end to end. You’ll lead the development of robust LangChain/LangGraph agentic workflows, high quality RAG pipelines, and scalable microservices on Google Vertex AI. You’ll own system design, implementation, MLOps, observability, and governance—partnering closely with product, data, security, and platform teams to deliver reliable, secure, and cost efficient AI products.
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.
Required Qualifications
• 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).
• Robust 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.
• Robust 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).