Role Title:
Band / Level:
Location:
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Role Overview
We are looking for a hands-on Full Stack AI Engineer to design, build, and deploy production-grade, AI-powered applications. In this role, you will bridge modern web interfaces with scalable Python backends and agentic workflows, taking features from concept to production on AWS cloud infrastructure.
Key Responsibilities
Frontend Architecture:
Develop responsive, type-safe user interfaces and conversational dashboards using React
and TypeScript
.Backend & API Development:
Design and implement robust backend services, microservices, and REST/GraphQL APIs in Python
, ensuring thorough test automation (unit, integration, and end-to-end).AI Orchestration & Agent Workflows:
Build multi-agent architectures, LLM orchestration layers, and evaluation pipelines using LangChain
, LangGraph
, and MLflow
.Cloud & Infrastructure as Code:
Provision, deploy, and manage scalable cloud resources on AWS
using AWS CDK
and containerize applications with Docker
.Data Management:
Model relational schemas, write high-performance SQL queries, and manage data persistence using PostgreSQL
; integrate analytics workflows with Databricks
.Quality & Collaboration:
Partner closely with data scientists, product managers, and cloud engineers to take prototypes to resilient, enterprise-ready deployments.
Required Qualifications & Experience
Experience:
5-10years of relevant full-stack software development experience.Frontend:
Strong proficiency in React
, TypeScript
, state management, and modern CSS/UI libraries.Backend:
Advanced Python
programming skills with a strong discipline around writing testable code (e.g., pytest, unittest, mocks).AI Stack:
Practical experience building LLM-driven applications using LangChain
and multi-agent coordination using LangGraph
; experiment tracking and model registry workflows with MLflow
.Databases:
Solid understanding of relational database design, query optimization, and schema migrations with PostgreSQL
.Cloud & DevOps:
Hands-on experience writing Infrastructure as Code (IaC) via AWS CDK
, along with containerization and image creation using Docker
.Data Platforms:
Practical exposure to data workflows and lakehouse environments in Databricks
.
Preferred / Nice-to-Have
- Experience setting up automated CI/CD pipelines via
GitHub Actions
. - Familiarity with
AgentCore
frameworks or custom autonomous agent design patterns. - Experience handling asynchronous event-driven architectures (e.g., SQS, Kafka, Celery).
Competencies & Attributes
- Strong analytical and problem-solving abilities.
- Focus on clean code, documentation, and automated testing.
- Collaborative mindset with strong communication skills across cross-functional engineering teams.
