SSE - Python + AWS + GenAI + Microservices/REST API
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About the Role
At Quest Global, it’s not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better place—to make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.
Key Responsibilities
AI‑First System Design & Software Engineering
- Apply strong software engineering principles to design, build, test, deploy, and operate AI‑powered systems.
- Architect clean, scalable, and maintainable services using microservices and distributed system design patterns.
- Own solutions end‑to‑end, ensuring reliability, observability, security, and maintainability in production.
Make informed engineering trade‑offs around scalability, latency, cost, and fault tolerance.
We are known for our extraordinary people who make the impossible possible every day. Questians are driven by hunger, humility, and aspiration. We believe that our company culture is the key to our ability to make a true difference in every industry we reach. Our teams regularly invest time and dedicated effort into internal culture work, ensuring that all voices are heard.
We wholeheartedly believe in the diversity of thought that comes with fostering a culture rooted in respect, where everyone belongs, is valued, and feels inspired to share their ideas. We know embracing our unique differences makes us better, and that solving the worlds hardest engineering problems requires diverse ideas, perspectives, and backgrounds. We shine the brightest when we tap into the many dimensions that thrive across over 21,000 difference-makers in our workplace.
Work Experience
We are looking for a Senior AI Engineer with strong software engineering fundamentals and a clear AI‑first, automation‑driven mindset. The ideal candidate will have deep hands‑on experience building production‑grade Generative AI and Agentic AI systems, and will naturally default to AI‑driven automation rather than manual, rule‑based, or UI‑heavy solutions.
This role focuses on designing, building, and operating scalable AI systems on AWS, applying sound engineering practices across the full SDLC.
Key Responsibilities
AI‑First System Design & Software Engineering
- Apply strong software engineering principles to design, build, test, deploy, and operate AI‑powered systems.
- Architect clean, scalable, and maintainable services using microservices and distributed system design patterns.
- Own solutions end‑to‑end, ensuring reliability, observability, security, and maintainability in production.
- Make informed engineering trade‑offs around scalability, latency, cost, and fault tolerance.
Generative AI & Retrieval‑Augmented Generation (RAG)
- Design and implement LLM‑based solutions for real‑world, production use cases.
- Apply deep hands‑on expertise in:
o Prompt engineering
- Chunking strategies for unstructured data
- Embeddings and vector databases
- Retrieval‑Augmented Generation (RAG)
- Graph RAG (knowledge graphs combined with vector search).
- Ensure AI systems are robust, explainable, and production‑ready.
Agentic AI & Workflow Automation
- Build Agentic AI systems capable of planning, reasoning, and executing multi‑step workflows.
- Implement tool and function calling, context management, and memory strategies for agents.
- Design systems where agents orchestrate workflows and LLMs act as reasoning and decision layers.
- Identify opportunities to replace manual or rule‑based processes with AI‑driven automation.
Cloud‑Native AI Platforms & AWS
- Build and deploy AI services on AWS using cloud‑native patterns.
- Work hands‑on with AWS services such as:
o EC2, Lambda, ECS, EKS
- DynamoDB, RDS (PostgreSQL/MySQL), MongoDB
- IAM, VPC, S3, EBS, CloudFront, Route 53.
- Leverage AWS Bedrock (Agents, Knowledge Bases) and Amazon SageMaker to operationalize AI solutions.
Containers, DevOps & CI/CD
- Containerize AI services using Docker and deploy using Kubernetes (EKS).
- Integrate services into CI/CD pipelines using GitHub or GitLab.
- Manage infrastructure using Infrastructure as Code (Terraform).
- Ensure stable, repeatable deployments in Linux environments.
Application Integration (Good to Have)
- Contribute to backend or application integration when required:
o Backend: Python (Flask) or Node.js (Express)
- Frontend: React or Angular.
- Integrate AI services with existing applications using APIs or events.
- UI and full‑stack work is supportive, not a primary responsibility.
Required Skills & Qualifications
- 5+ years of experience in Software Engineering or AI Engineering.
- Strong software engineering skills with proven experience building production systems.
- Strong programming experience in Python.
- Hands‑on experience with:
o Generative AI and Large Language Models
- RAG pipelines, embeddings, and vector databases
- Agentic AI workflows and systems.
- Solid understanding of:
o System design
- Microservices and distributed systems.
- Experience deploying and operating systems on AWS.
- Comfortable working in Agile environments and Linux systems.
Good to Have
- Full‑stack development experience (React/Angular, Flask/Node.js)
- Knowledge graph experience
- MLOps exposure
- Experience working within enterprise AI governance frameworks
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