FSE AI Tech Lead - W2 Only

Saransh Inc; Atlanta (GA); Hybrid; USD 170,000 - 230,000; Full time

Job summary

Saransh Inc. is seeking a senior FSE AI Tech Lead in Atlanta, GA. The role combines hands-on full stack development with AI-driven product features, guiding teams through spec-first workflows and cloud-based deployments.

The ideal candidate brings 12+ years of experience across React/Node.js/Python, AWS, and databases, plus leadership in delivering complex multi-service systems and MLOps-aware practices.

Qualifications

• 12+ years of professional software engineering experience with full stack background.
• GenAI tools and spec-first development approach incl. GitHub Spec Kit or equivalent.
• Expert-level proficiency in React and modern JS/TS frameworks.
• Strong backend experience with Node.js and Python REST/GraphQL APIs.
• Deep AWS experience (Lambda, EC2, S3, API Gateway, RDS).
• Experience designing/managing MongoDB and PostgreSQL at scale.
• Ability to integrate LLM APIs and build prompt engineering pipelines.
• Track record leading technical delivery and owning outcomes.
• Bachelor’s or Master’s in CS/Engineering or equivalent practical experience.

Responsibilities

• Drive spec-first development practices across teams with specs and task breakdowns.
• Architect and build full stack web apps using React frontend and Node.js/Python backend.
• Design, develop, and maintain RESTful and GraphQL APIs with performance and security.
• Lead cloud architecture and deployment on AWS with scalable services.
• Integrate AI-powered features using LLMs, AI agents, and prompt engineering.
• Own data architecture decisions for MongoDB and PostgreSQL; optimize schemas.
• Mentor engineers, conduct code reviews, raise engineering bar.
• Partner with product/design/AI/ML to translate requirements into software.
• Contribute to engineering strategy, tooling, and cross-team standards.

Skills

GenAI & Spec-First Dev

Full Stack Engineering

Cloud & Data

REST & GraphQL APIs

Education

Bachelor's or Master's in CS/Engineering

Tools

React

Node.js

Python

AWS

MongoDB

PostgreSQL

GraphQL

Job description

Role: FSE AI Tech Lead

Location: Atlanta, GA

Hybrid or Remote is acceptable.

W2 Position

Name

Python

TypeScript

AI/ML

Java Script

AI

Node JS

React

What are the top 3 skills required for this role?

• 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.
• 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.
• 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.