Full Time AI Developer – Agentic AI (AWS Bedrock) (4 Months Contract) Jobs, in Rhino Partners - Maukerja

Rhino Partners is looking for an experienced AI Developer (Consultant) to design, develop and deploy production-ready Agentic AI solutions using AWS technologies in a government organisation.

In this role, you will build intelligent AI applications and multi-agent workflows using Amazon Bedrock, Bedrock Guardrails and Amazon Bedrock AgentCore. You will work closely with architects, product owners and security teams to deliver secure, scalable and reliable AI solutions that integrate with enterprise systems.

We are looking for a hands-on engineer with strong Python development skills, practical experience building Generative AI applications and a solid understanding of cloud-native software engineering.

Agentic AI Development

• Design, develop and deploy Agentic AI applications and multi-agent workflows using Amazon Bedrock and frameworks such as Strands Agents, LangChain or LangGraph.
• Integrate Large Language Models (LLMs) using Bedrock Runtime APIs, including model invocation, streaming, prompt engineering, function calling and structured outputs.
• Build Retrieval-Augmented Generation (RAG) pipelines using Bedrock Knowledge Bases, vector databases and custom retrieval strategies.
• Implement agent capabilities including tool orchestration, planning, memory management and multi-step reasoning.

AWS Bedrock & AgentCore Engineering

• Deploy and manage AI agents using Amazon Bedrock AgentCore Runtime, Gateway and Memory.
• Implement secure authentication and authorisation using AgentCore Identity and AWS IAM.
• Integrate AgentCore Code Interpreter and Browser capabilities where required.
• Configure and optimise Bedrock Guardrails, including content filtering, PII protection, contextual grounding and safety controls.

Cloud Integration & DevOps

• Develop APIs and integrate AI applications with enterprise systems using AWS Lambda, API Gateway, Step Functions and event-driven architectures.
• Implement monitoring and observability using Amazon CloudWatch, AgentCore Observability and distributed tracing.
• Build and maintain CI/CD pipelines, automated tests and containerised deployments.
• Troubleshoot and optimise AI application performance, reliability, accuracy, latency and operational costs.

Quality, Security & Collaboration

• Develop clean, maintainable and well-tested Python code following software engineering best practices.
• Conduct automated evaluations and regression testing for prompts, agents and AI safety controls.
• Apply AI security best practices to mitigate risks such as prompt injection, data leakage and unauthorised tool access.
• Collaborate with cross-functional teams on solution design, technical implementation and production support.
• Participate in code reviews, technical documentation and continuous improvement initiatives.

• 4–7 years of software development experience, including at least 1 year of hands-on experience developing Generative AI or LLM-powered applications.
• Proven experience building Agentic AI solutions, including tool calling, multi-step workflows, memory and agent orchestration.
• Hands-on experience with Amazon Bedrock, including foundation model integration, Converse API, Knowledge Bases and Agents.
• Experience implementing and testing Bedrock Guardrails for AI safety, privacy and contextual grounding.
• Familiarity with Amazon Bedrock AgentCore, including Runtime, Gateway, Memory, Identity and Observability.
• Strong proficiency in Python, including boto3, asynchronous programming and API development using FastAPI or similar frameworks.
• Good working knowledge of AWS services such as Lambda, S3, DynamoDB, API Gateway, IAM, CloudWatch and ECR.
• Experience with agent frameworks such as Strands Agents, LangChain, LangGraph, CrewAI or AutoGen.
• Understanding of RAG architectures, embeddings, vector databases, retrieval optimisation and context management.
• Familiarity with Git, Docker, CI/CD pipelines and automated testing.
• Strong problem-solving, communication and collaboration skills, with the ability to work independently in an Agile environment.
• Degree in Computer Science, Engineering or a related discipline, or equivalent practical experience.

• Experience with Model Context Protocol (MCP) server/client integrations.
• Experience with AWS infrastructure-as-code tools such as Terraform, AWS CDK or CloudFormation.
• Familiarity with vector databases such as Amazon OpenSearch or pgvector.
• Experience with LLM evaluation frameworks, red-teaming and adversarial testing.
• Knowledge of AI security principles, including prompt injection prevention, data protection and least-privilege access.
• Experience developing AI application interfaces using React, Streamlit or TypeScript/Node.js.
• Relevant AWS certifications, such as AWS Certified Developer – Associate, AWS Certified AI Practitioner or AWS Certified Machine Learning – Specialty.