Senior AI Engineer
Summary
Builds and deploys agentic AI systems, integrating LLMs and automation tools into enterprise workflows while ensuring reliability, security, and scalability across full-stack applications.
A leading telecommunications and technology enterprise is undertaking a major strategic transformation to embed artificial intelligence and data analytics into the core of its business. Through a dedicated central AI and data organization, the company is focused on scaling agentic AI, building enterprise-wide AI literacy, and augmenting human capability across its consumer, enterprise, IT, network, and corporate divisions. They foster an agile, innovation-driven culture that puts human potential at the heart of technology evolution.
- Solution Design & Implementation: Develop end-to-end AI workflows, agentic AI solutions, and hybrid systems combining front-end/back-end software, LLMs, prompt engineering, and automation tools for human-in-the-loop processes.
- System Integration & Architecture: Collaborate with Solution Architects and Lead Engineers to integrate AI solutions into enterprise ecosystems using APIs, Model Context Protocol (MCP), and middleware components (including SSO, OAuth2, and JWT).
- Full-Stack Development: Maintain and continuously enhance user interfaces for usability while applying modular, high-standard software engineering practices across back-end pipelines and front-end applications.
- Operations & Monitoring: Monitor production AI services and recommendation engines, track KPIs, ensure high availability, and proactively resolve reliability, scalability, or performance issues.
- Security & Risk Mitigation: Enforce security best practices, build enterprise-grade integrations, establish fallback strategies, and manage risk across production systems.
- Applied R&D & Innovation: Tackle novel, untested problems through applied R&D, stay current with advancing AI/agentic frameworks, and bring continuous improvements into live enterprise environments.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data, AI/ML, or a related field.
- 3–5 years of hands-on experience in software development and AI deployment, with proven exposure to agentic AI workflows and marketplace/recommender systems.
- Languages: Python and SQL.
- AI/ML Frameworks: LangChain, LangGraph, or equivalent agentic and LLM orchestration tools, alongside strong prompt engineering knowledge.
- Integrations & Middleware: RESTful APIs, gRPC, MCP, SSO, OAuth2, and JWT.
- Engineering Tools: Cloud environments (AWS, Azure, or GCP) and Git version control (GitHub, GitLab, Bitbucket).
- Strong balance of operational ownership, R&D curiosity, and problem-solving skills.
- Ability to navigate project timelines alongside governance and process approvals.Excellent collaboration skills for working with cross-functional technical and business teams.