AI Architect C2C jobs- Piscataway, New Jersey ( Local Candidates only )

12+ years in software engineering, and AI architecture, with 3+ years designing and delivering GenAI/LLM solutions and at least two enterprise AI implementations taken to production.

AI Architect to define how enterprise AI gets built — and built to last. You will own the target-state architecture for client AI programs, set the platform, model, and governance direction, and guide engineering teams from discovery and PoC through to secure, scalable, production-grade agentic solutions.

The role sits at the intersection of AI platform architecture, solution design, and business value — best suited to a seasoned technologist who can make sound architectural trade-offs under ambiguity, still writes code when it matters, and is the trusted technical authority for client CXOs, enterprise architects, and delivery teams.

Deep expertise in agentic system design — single and multi-agent orchestration, planning and reasoning strategies, tool/function calling, MCP, memory, guardrails, and human-in-the-loop patterns.

Strong command of RAG and context engineering at scale: chunking and indexing strategy, embeddings, vector databases, hybrid and re-ranked retrieval, knowledge graphs, and retrieval evaluation.

Hands-on with LangChain/LangGraph, LlamaIndex, CrewAI, AutoGen/Semantic Kernel or equivalent frameworks, plus OpenAI/Anthropic APIs, Hugging Face, and open-weight models.

Strong Python engineering and API design (FastAPI or similar), microservices and event-driven integration patterns, and enterprise system integration.

Production experience with Docker, Kubernetes (EKS/AKS/GKE), Terraform or equivalent IaC, CI/CD, and Git-based engineering practices.

Proven LLMOps/AgentOps experience: evaluation frameworks, observability and tracing (e.g. LangSmith, Langfuse, Arize, MLflow), prompt/model lifecycle, and cost and latency optimisation.

Architecture-level depth across frontier-model providers and hyperscaler AI platforms, with proven production depth in at least one

Able to justify platform and model choices against client constraints — capability, context window, latency, cost per interaction, data residency, enterprise controls, and existing cloud commitments.

Proven ability to own architecture decisions, document them clearly (ADRs, reference architectures, HLD/LLD), and defend them with business and technical stakeholders.

Excellent communication and executive presence; able to hold a CXO room, an enterprise architecture board, and an engineering stand-up with equal credibility.

Track record of leading and mentoring cross-functional AI and engineering teams across multiple concurrent engagements.

Experience with modern data platforms and their GenAI surfaces (Databricks Mosaic AI, Snowflake Cortex, lakehouse patterns) and data governance tools.

Certification as a cloud solutions architect or on a hyperscaler AI/ML track (AWS, Azure, or Google).

Pre-sales, consulting, or forward-deployed engineering experience, including proposal and SoW ownership.

Self-hosted / open-weight model deployment and optimisation (vLLM, Ollama, TGI, quantisation, GPU sizing) for strict data-boundary requirements.

Familiarity with AI regulation and standards such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001.