AI Engineer

Role overview

Responsibilities

• check_circle Design, develop and deliver AI-enabled features, applications and agent-driven workflows.
• check_circle Build production-grade AI solutions using LLM APIs, cloud AI services and modern AI frameworks.
• check_circle Design agentic AI architectures incorporating reasoning loops, tool use, memory, guardrails and observability.
• check_circle Integrate LLM platforms such as OpenAI and Anthropic into enterprise applications.
• check_circle Develop robust backend services and AI applications using Python.
• check_circle Collaborate with engineering, product and business teams to understand requirements and deliver practical AI solutions.
• check_circle Design and implement evaluation frameworks to measure AI model quality, reliability, safety and performance.
• check_circle Develop RAG-based solutions using vector databases, embeddings and retrieval pipelines.
• check_circle Identify and engineer for AI failure modes, latency, cost, security and operational risks.
• check_circle Implement monitoring and observability for AI systems after deployment.
• check_circle Apply security, testing and compliance practices throughout the AI development lifecycle.
• check_circle Use AI-assisted development tools to improve engineering productivity and code quality.
• check_circle Conduct code reviews and contribute to engineering standards, reusable frameworks and best practices.
• check_circle Support the transition of AI solutions from experimentation and prototyping into production.
• check_circle Work closely with distributed engineering teams and stakeholders across different locations.
• check_circle Provide technical leadership and contribute to continuous improvement initiatives.

Basic qualifications

• Strong software engineering background with extensive hands-on Python development experience.
• Proven experience developing and deploying production-grade AI or machine learning applications.
• Strong experience integrating LLM APIs, particularly OpenAI or similar platforms.
• Hands-on experience building Agentic AI or tool-using AI systems.
• Experience with AI agent orchestration, memory, tools and workflows.
• Experience designing and implementing AI evaluations (evals).
• Strong understanding of LLM reliability, safety, quality and regression testing.
• Experience with Retrieval-Augmented Generation (RAG).
• Experience with vector databases, embeddings and retrieval technologies.
• Strong understanding of cloud-based AI services and modern AI frameworks.
• Experience designing resilient and scalable production systems.
• Strong understanding of software engineering, testing, security and DevOps practices.
• Ability to work across backend services and modern application environments.
• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder collaboration skills.

Preferred qualifications

• Experience with AWS Bedrock, Azure AI Foundry or similar managed AI platforms.
• Knowledge of AI interoperability standards such as MCP and A2A.
• Experience with Docker and Kubernetes.
• Experience with CI/CD and modern DevOps tooling.
• Experience with cloud-native application development.
• Experience with AI-assisted coding tools.
• Knowledge of AI governance, security, guardrails and responsible AI practices.
• Experience working in enterprise or regulated environments.
• Active interest in emerging AI technologies and engineering practices.