Senior Applied AI Engineer

Role Overview

We are looking for a Senior Applied AI Engineer to design and develop practical AI solutions for enterprise clients.

The role focuses on building production-ready AI applications using large language models, RAG, AI agents and workflow automation, and integrating these capabilities into existing enterprise systems.

Key Responsibilities

• Design and develop enterprise AI applications using large language models.

• Build RAG systems, AI assistants, AI agents and workflow automation solutions.

• Integrate AI models with enterprise applications, databases and third-party APIs.

• Develop prompt workflows, structured outputs and tool-calling functions.

• Design document processing, embeddings, vector search and retrieval pipelines.

• Implement AI evaluation, testing and guardrails.

• Optimise AI performance, reliability, latency and operating cost.

• Develop backend APIs and supporting services for AI applications.

• Work closely with solution architects, business analysts and developers on project delivery.

• Support deployment, monitoring, troubleshooting and continuous improvement of AI systems.

Requirements

• Bachelor’s degree in Computer Science, Artificial Intelligence, Information Technology or a related field.

• At least 3 years of relevant professional experience in AI engineering, machine learning, data engineering or enterprise AI application development.

• Strong programming skills in Python.

• Hands-on experience with LLM-based application development.

• Experience with OpenAI API, Anthropic API, Amazon Bedrock or similar AI platforms.

• Practical experience with RAG, embeddings and vector databases.

• Experience with structured outputs, function calling or tool calling.

• Strong understanding of APIs, databases and backend application development.

• Experience with AWS, Azure or Google Cloud.

• Good problem-solving and communication skills.

Preferred Skills

• Amazon Bedrock

• LangGraph, LangChain or similar frameworks

• PostgreSQL and pgvector

• Amazon OpenSearch

• Vector databases

• FastAPI

• Document parsing and data ingestion pipelines

• AI evaluation and regression testing

• Prompt injection protection and AI guardrails

• AWS Lambda, S3, DynamoDB, SQS and Step Functions

• Docker and CI/CD