Full Stack Engineer (AI Developer) - #1773

Job Description | Consultant

Role Overview

We are looking for an AI Developer to build, test and deploy intelligent agentic applications on AWS. You will work with Amazon Bedrock foundation models, Bedrock Guardrails and Amazon Bedrock AgentCore to deliver secure, reliable and scalable AI solutions.

You will be a core member of the AI engineering team, writing production-quality code and collaborating with architects, product owners and security teams.

Key Responsibilities

• Develop agentic AI applications and multi-agent workflows using Amazon Bedrock and agent frameworks such as Strands Agents, LangChain / LangGraph.
• Integrate LLMs via the Bedrock Runtime APIs (InvokeModel, Converse, streaming) with robust prompt engineering, tool/function calling and structured outputs.
• Build RAG pipelines using Bedrock Knowledge Bases, vector stores and custom retrieval logic.
• Configure and tune Bedrock Guardrails (content filters, denied topics, PII filters, contextual grounding checks) and apply the ApplyGuardrail API in agent and application flows.
• Deploy agents to Amazon Bedrock AgentCore Runtime; expose tools and APIs through AgentCore Gateway; implement session and long-term memory with AgentCore Memory.
• Implement authentication and authorisation for agents and tools using AgentCore Identity and AWS IAM.
• Use AgentCore Code Interpreter and Browser tools where agents need to execute code or interact with web content securely.
• Instrument agents with AgentCore Observability, CloudWatch and tracing to monitor latency, token usage, cost and tool-call behaviour.
• Build and run automated evaluations and regression tests for prompts, agents and guardrails.
• Develop APIs and integrations with enterprise systems using Lambda, API Gateway, Step Functions and event-driven patterns.
• Write clean, tested, documented code; participate in code reviews and CI/CD pipelines.
• Troubleshoot and optimise agent performance, accuracy, safety and cost.

Required Skills & Experience

• 4+ years of software development experience, with 1+ year building Generative AI / LLM applications.
• Hands-on experience developing Agentic AI solutions: tool use, planning, memory, multi-step reasoning and multi-agent collaboration.
• Practical experience with Amazon Bedrock (model invocation, Converse API, Knowledge Bases, Agents).
• Experience configuring and testing Bedrock Guardrails for safety, privacy and grounding.
• Working knowledge of Amazon Bedrock AgentCore (Runtime, Gateway, Memory, Identity, Observability, Code Interpreter, Browser).
• Strong Python proficiency (boto3, async programming, FastAPI or similar); TypeScript/Node.js a plus.
• Good understanding of AWS services: Lambda, S3, DynamoDB, API Gateway, IAM, CloudWatch, ECR.
• Familiarity with Git, CI/CD and containerisation (Docker).

Technical Expertise

• LLM development: prompt engineering, structured outputs, function calling, streaming, context management.
• Agent frameworks: Strands Agents SDK, LangChain / LangGraph, CrewAI or AutoGen; MCP server/client integration.
• AWS Bedrock: Converse API, Knowledge Bases, Guardrails (including ApplyGuardrail), Prompt Management, Flows.
• AgentCore: Runtime deployment, Gateway tool exposure, Memory strategies, Identity, Observability.
• Data & retrieval: embeddings, vector databases (OpenSearch, pgvector), chunking strategies, reranking.
• DevOps: Docker, CDK / Terraform / CloudFormation, GitHub Actions / CodePipeline, unit and integration testing.

Preferred Qualifications

• AWS Certified Developer – Associate, AWS Certified AI Practitioner or AWS Certified Machine Learning – Specialty.
• Experience with LLM evaluation tools and red-teaming / adversarial testing of AI systems.
• Knowledge of security best practices for AI: prompt injection, data leakage and least-privilege access.
• Experience with front-end integration (React, Streamlit) for AI applications.

Soft Skills

• Strong problem-solving and analytical skills.
• Collaborative, curious and keen to learn rapidly evolving technology.
• Clear written and verbal communication.
• Ownership mindset with attention to quality and safety.

Education

Degree in Computer Science, Engineering or a related field (or equivalent practical experience).