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AI Engineer (FastAPI & AWS)
A hands-on engineering role. You'll design, build, and scale production-grade REST APIs that expose AI and LLM capabilities to our enterprise and government clients, owning the full API lifecycle on FastAPI, Pydantic, and AWS.
At a Glance
• Location: India (preferred, Hybrid); other locations considered
• Employment type: Full-time, permanent
• Experience: Mid to senior level, over 10 years, including production FastAPI and AWS experience
• Role type: Hands-on engineering, not a people-management role
• Core focus: FastAPI and Pydantic REST APIs, AWS Bedrock LLM integration, and scalable deployment on EC2, Lambda, and EMR
• Working hours: Shifted India hours with at least 4 hours' daily overlap with Australian (Melbourne and Brisbane) business hours, typically around 7:30am–4:30pm IST, adjusted for Australian daylight saving
About Power Tech Consulting
Power Tech is a data, AI, cloud, and transformation consultancy serving mining, financial services, hospitality, and government clients across Australia, New Zealand, Fiji, Singapore, and India. We're multi-vendor, so we recommend what's right, not what's easy to sell, and we're building our own products alongside client work.
Service through contribution. Trust through transparency. Accountable from end to end. Join us and help shape what Power Tech becomes.
About the Role
Power Tech Nexus is the product company of the Power Tech group. We build and run our own AI-driven software products for businesses in Australia and the region. We are a small team growing deliberately, so every hire shapes how the product and the engineering practice turn out.
Our clients increasingly want AI capabilities they can rely on in production, delivered through fast, secure, well-documented services. As AI Engineer (FastAPI & AWS), you'll own the full API lifecycle, from schema design and endpoint implementation to deployment and scaling on AWS. You'll work alongside our data engineers, architects, and applied-AI research team.
Key Responsibilities
• Design and build RESTful AI APIs in FastAPI, implementing full CRUD resources (GET, POST, PUT, PATCH, and DELETE).
• Define request and response schemas, validation, and serialisation using Pydantic models.
• Integrate AWS Bedrock foundation models and other LLM services behind clean, versioned API contracts.
• Design async, non-blocking endpoints for high-throughput and streaming AI workloads.
• Deploy and scale API services on AWS EC2 and Lambda, including auto-scaling and load-balancing configuration.
• Build and orchestrate data-processing workflows on AWS EMR to support AI pipelines.
• Implement authentication, authorisation, rate-limiting, and API versioning, using OAuth2, JWT, and API keys.
• Write automated unit, integration, and load tests, and maintain OpenAPI and Swagger documentation.
• Implement caching, connection pooling, and performance tuning so APIs stay scalable and low-latency.
• Set up CI/CD, logging, monitoring, and observability for API services in production.
Must-Have Skills and Experience
• Strong Python and backend software-engineering fundamentals, including async Python (asyncio, async/await) for high-concurrency API design.
• Hands-on production experience building REST APIs with FastAPI, including full CRUD endpoint design.
• Strong command of Pydantic for data validation, schema modelling, and serialisation.
• Hands-on experience with AWS Bedrock for generative AI and LLM integration.
• Practical experience with AWS EC2, Lambda, and EMR for hosting and scaling API and data workloads.
• Experience with API security, authentication and authorisation, and rate-limiting patterns.
• Experience with cloud-native deployment, containerisation with Docker, and infrastructure-as-code.
• Understanding of API scalability patterns: load balancing, caching, horizontal scaling, and queuing.
Desirable Skills
• Experience with GenAI frameworks such as LangChain, LangGraph, or CrewAI, and with RAG architectures.
• Familiarity with vector databases such as Pinecone, FAISS, or pgvector.
• AWS API Gateway experience, and event-driven architectures using SQS, SNS, or EventBridge.
• MLOps or LLMOps tooling and model monitoring.
• AWS certifications such as AWS Certified AI Practitioner or Solutions Architect, and responsible-AI or AI-governance experience.
Education
A bachelor's degree in computer science, engineering, or a related field, or equivalent experience.
What Success Looks Like
• The AI APIs you build run reliably in production, meeting their latency and throughput targets as client usage grows.
• Your API contracts are clean, versioned, and well documented, so client and internal teams can integrate without handholding.
• Security, testing, and observability are built into your services from the start, not added after an incident.
• Your patterns for FastAPI, Pydantic, and Bedrock become reusable building blocks across our AI engagements.