Artificial Intelligence Engineer

About the role

This role owns the design, development, and deployment of production-ready AI and Machine Learning solutions, including Generative AI applications, intelligent agents, and AI-powered products. It requires strong execution across model development, MLOps, cloud deployment, and software engineering so solutions are not only accurate but also scalable, monitored, and maintainable. The successful candidate will work across the full delivery lifecycle, from experimentation and pipeline design to APIs, microservices, and production monitoring.

Responsibilities

• Design, develop, and deploy AI and machine learning models for production use so business teams can rely on solutions that are stable, measurable, and built for scale.
• Create Generative AI applications, copilots, and intelligent agents that improve knowledge access, task automation, and user experience across business workflows.
• Develop RAG pipelines using vector databases and large language models to improve retrieval quality, response relevance, and knowledge-grounded outputs.
• Create scalable data pipelines, feature engineering workflows, and model training pipelines that support repeatable experimentation and faster delivery.
• Develop APIs and microservices using Python so AI capabilities can be integrated cleanly into enterprise applications and digital products.
• Deploy and monitor models using MLflow, Docker, and CI/CD to keep releases controlled, observable, and production-ready.
• Operationalize models in cloud environments with strong reliability and governance using Azure ML, SageMaker, or Vertex AI.
• Collaborate with data scientists, engineers, and business stakeholders to align AI solutions with real priorities, delivery constraints, and measurable impact.

Requirements

The successful candidate should have 2–6 years of AI/ML engineering experience and can work confidently across model development, deployment, and production support without losing sight of business outcomes.

Qualifications

You combine strong coding discipline with practical AI judgment, making trade-offs that improve reliability, scalability, and delivery speed.

Skills

• Strong Python programming skills with the ability to build production-grade services, automation, and AI workflows that are maintainable and efficient.
• Hands-on experience with TensorFlow, PyTorch, and Scikit-learn to develop, train, evaluate, and refine machine learning and deep learning models.
• Practical knowledge of machine learning, deep learning, statistics, and feature engineering to convert data into robust and useful predictive systems.
• Experience with MLOps practices using MLflow, Docker, and CI/CD so model deployment, monitoring, and iteration happen in a controlled and repeatable way.
• Experience with Azure ML, AWS SageMaker, or Google Vertex AI to run models on cloud AI platforms with production readiness in mind.
• Knowledge of LLMs, prompt engineering, RAG, and vector databases to support modern Generative AI use cases with grounded responses.
• Experience building REST APIs and microservices so AI capabilities can be exposed reliably to products, platforms, and internal systems.
• Familiarity with modern AI engineering tools such as LangChain, LangGraph, LlamaIndex, Pinecone, FAISS, ChromaDB, Milvus, FastAPI, Flask, Kubernetes, or Azure OpenAI Services to move solutions faster from prototype to deployment.

Plus

• Exposure to LangChain, LangGraph, or LlamaIndex is a strong advantage for building more capable Generative AI workflows and orchestration layers.
• Experience with Pinecone, FAISS, ChromaDB, or Milvus is valuable for designing retrieval systems that improve answer relevance and knowledge access.
• Familiarity with Kubernetes and Azure OpenAI Services is a plus for teams operating at enterprise scale with modern cloud-native AI stacks.

Benefits

The organization values practical innovation, cross-functional collaboration, and measurable impact through AI-powered products, copilots, intelligent agents, and automated workflows.

Pay

Details TBD

Schedule

Details TBD