Python Full stack AI Lead - Only W2

We are not considering C2C candidates for this role. The candidate must be willing to work on our W-2 from day one. 

Position:- Full stack Ai Lead

Location:- Princeton, NJ

Onsite / Hybrid

Client need 10+ yr exp 

What are the top 3 skills required for this role?
1.GenAI & Spec-First Development — deep expertise in spec-driven workflows (GitHub Spec Kit or equivalent), AI agent orchestration, and building AI-powered product features end to end.
2.Full Stack Engineering (React / Node / Python) — extensive experience delivering production applications across the entire stack, from modern React UIs to Node.js and Python backends with robust API layers.
3.Cloud & Data (AWS + MongoDB / PostgreSQL) — strong command of AWS infrastructure and both relational and document databases, including schema design, optimisation, and cloud-native deployment patterns.

Job Description/ Responsibilities

•Drive spec-first development practices across teams — leading the authoring of specs, technical plans, and agent-ready task breakdowns using GitHub Spec Kit or equivalent tooling before any code is written.
•Architect and build full stack web applications using React and modern JavaScript / TypeScript frameworks on the frontend, backed by Node.js and Python services.
•Design, develop, and maintain RESTful and GraphQL APIs — ensuring performance, reliability, versioning, and security across all service boundaries.
•Lead cloud architecture and deployment on AWS, leveraging services such as Lambda, EC2, S3, API Gateway, RDS, and CloudFormation for scalable, resilient systems.
•Integrate and build AI-powered features using LLMs, AI agents, and prompt engineering techniques, translating GenAI capabilities into tangible product value.
•Own data architecture decisions across MongoDB and PostgreSQL, including schema design, indexing strategies, query optimization, and migrations.
•Mentor and technically guide engineers at all levels, conducting code reviews and raising the overall engineering bar across the organization.
•Partner with product, design, and AI/ML teams to define requirements and translate them into well-specified, high-quality software.
•Contribute to engineering strategy, tooling choices, and cross-team standards as a senior technical leader.


Required Qualifications

•12+ years of professional software engineering experience with a strong full stack background.
•Proven experience with GenAI tools and a spec-first development approach — including GitHub Spec Kit, AI agent frameworks, or equivalent spec-driven methodologies.
•Expert-level proficiency in React and modern JavaScript / TypeScript frameworks (Next.js, Vue, or similar).
•Strong backend development experience with both Node.js and Python — building, maintaining, and scaling production-grade REST and GraphQL APIs.
•Deep, hands-on experience with AWS — comfortable across core services (Lambda, EC2, S3, API Gateway, RDS) as well as security, networking, and cost optimization.
•Solid experience designing and managing both MongoDB (document store) and PostgreSQL (relational) databases at scale.
•Demonstrated ability to integrate LLM APIs (OpenAI, Anthropic, or similar), build prompt engineering pipelines, and deliver AI-augmented product features.
•Track record of leading technical delivery — setting architecture direction, unblocking teams, and owning outcomes across complex, multi-service systems.
•Bachelor’s or master’s degree in computer science, Engineering, or equivalent practical experience.


Good to Have

•Experience with GitHub Copilot, Cursor, or AI-assisted development environments integrated into day-to-day engineering workflows.
•Familiarity with containerization (Docker, Kubernetes) and infrastructure-as-code tools (Terraform, AWS CDK).
•Exposure to vector databases (Pinecone, pgvector) or RAG (Retrieval-Augmented Generation) pipeline design.
•Experience with AI orchestration frameworks such as LangChain or LlamaIndex.
•Knowledge of event-driven architecture patterns using AWS SQS, SNS, or EventBridge.
•Familiarity with MLOps practices and deploying ML models into production pipelines.
•Contributions to open-source projects, technical writing, or a portfolio of AI-integrated applications.