Job Description

We are looking for an AI Engineer with hands-on experience designing and deploying scalable AI solutions. In this role, you will be part of a cross-functional team working on cutting-edge projects involving Retrieval-Augmented Generation (RAG), agentic frameworks, and end-to-end MLOps workflows.

You'll play a key role in developing AI applications using tools like LangChain, CrewAI, and Google ADK, while applying advanced prompt engineering techniques and ensuring robust monitoring and performance tracing. Your collaboration with other engineering will help align innovative AI systems with broader development goals.

What is this position about

Analyze business problems and design AI solutions from scratch, including architecture, approach selection, and technical feasibility assessment.
Design, develop, and deploy production-ready AI solutions with focus on scalability and reliability.
Implement Retrieval-Augmented Generation (RAG) systems and fine-tuning techniques.
Build and integrate AI frameworks and agentic systems to solve complex business problems.
Apply advanced prompt engineering techniques and output post-processing strategies.
Design and implement MLOps/LLMOps pipelines including CI/CD, model versioning, and monitoring.
Lead end-to-end project delivery and mentor junior engineers.
Work with transformer-based models and optimize LLM integration patterns.
Design APIs, microservices, and event-driven architectures for AI systems.
Collaborate across teams to translate AI capabilities into business value.
Stay current with AI trends and contribute to best practices

Qualifications

4+ years total experience with at least 1 year as a Data Scientist and remaining years in ML/AI Engineering or MLOps roles.
Proven experience working with early LLM architectures (BERT, RoBERTa, GPT-2, T5, or similar transformer models).
Demonstrable track record of deploying LLM-based models to production environments.
Proven ability to design AI solutions from scratch given business problems, including architecture design and approach selection.
Expert-level Python programming and strong software engineering fundamentals.
Experience with Azure cloud services and containerization for AI/ML workloads.
Hands-on experience with MLOps tools and practices (model deployment, monitoring, experiment tracking).
Strong understanding of both data science methodologies and production system design.
Experience with version control (Git) and Agile/Scrum development practices.
Excellent communication and stakeholder management skills.
Ability to lead projects independently and mentor team members.

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