Strategic Staffing Solutions is currently looking for a Senior AI Developer for a W2 contract opportunity with one of our largest clients!
This is a W2 contract opportunity, and the candidates should be willing to work on our W2 ONLY, NO C2C.
Senior AI Developer
Location: Charlotte, NC
Role: W2 contract
Duration: 12 months
Job Title: Senior AI Developer (Full Stack)
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, ML Ops, 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
- 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, We aviate, 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/Fast API, 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.
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).
- Strong RAG practitioner (chunking strategies, embeddings, hybrid retrieval, Re rankers like Cohere/Re rank or bge re rank, evaluation).
- Deep experience with vector databases (Pinecone, We aviate, Milvus, FAISS) and embedding models (OpenAI, Vertex, Cohere, bge large).
- Production backends in Python (Fast API) 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 (Lang Smith/Llama Index observability/Open Telemetry), and prompt/version management.
- Knowledge of security & governance: PII handling, isolation, data residency, prompt injection defenses, secret management.
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