Overview
The AI Solutions Analyst will contribute to the design, development, and operation of next-generation AI-enabled platforms and services, building AI-driven solutions using foundational models and agentic AI approaches.
Key Responsibilities
- Build AI-powered solutions leveraging hyper-scaler foundational AI services.
- Apply foundation models and agentic AI patterns to solve real-world development challenges.
- Implement sandboxed environments to safely test and evaluate AI agents.
- Ensure agents operate within controlled boundaries.
- Contribute to building AI agent frameworks, SDKs, and reusable components.
- Assist in the design, fine-tuning, and evaluation of machine learning models & SLMs via MLOps.
- Support experimentation workflows for context engineering, model evaluation, and iterative improvements.
- Contribute to responsible AI practices.
- Help operationalize AI solutions using CI/CD deployment pipelines.
- Leverage agent orchestration and agent harness patterns.
- Participate in designing APIs and services that expose AI capabilities.
- Support observability through AI system monitoring.
- Work under guidance of senior engineers.
- Gain exposure to enterprise-scale AI applications.
- Support development, testing, and deployment of AI/ML models and platform components.
- Assist in building and enhancing internal AI platforms and self-service capabilities.
- Implement and support monitoring, observability, and operational reliability practices.
- Collaborate with cross-functional teams.
- Enable delivery of self-service production-grade AI/ML solutions.
- Develop Agentic AI Core & Foundational guard rails.
- Experiment with AI industry trends.
- Leverage modern Agent Harness to safely deploy Agentic AI use cases.
Required Experience
- 2 years of experience or equivalent combination of education and experience (for example, in the IT field: Bachelor’s Degree with a minimum of 1 year of related work experience).
- Demonstrated exposure to AI/ML, cloud computing, or DevOps practices, with a clear interest in building scalable, production-ready systems.
- Proficiency in at least one programming language (e.g., Python, Node.js, Go or similar), with a solid foundation in software engineering, cloud architecture, and modern development practices (Git, APIs).
- Working knowledge of cloud platforms (Azure, AWS, GCP), including automation, CI/CD pipelines, containerization, and infrastructure-as-code.
- Understanding of API design, API Management (APIM), API gateways, and API security.
- Familiarity with AI Gateway concepts, token consumption models, and observability tools.
- Demonstrated interest and working knowledge in Artificial Intelligence, including Generative AI, Large Language Models (LLMs), machine learning, and Natural Language Processing (NLP).
- Experience or exposure to automation, platform engineering, and scalable system design, with awareness of microservices architecture and deep learning concepts.
Qualifications
- Bachelor’s or Master’s degree.
Recommended Certifications:
- Microsoft Azure Fundamentals (AZ-900) or AWS Certified Cloud Practitioner
- Azure AI Fundamentals (AI-900) or equivalent
- Kubernetes or Docker introductory certifications
- Any entry-level certification in AI Engineering, DevOps or Cloud Engineering
