Design, build, and deploy AI agents and AI-enabled services.
● Translate business workflows into automated and AI-assisted systems.
● Integrate LLMs with internal platforms, APIs, and data sources.
● Start with rules and automation. Add AI only where it adds clear value.
● Develop AI services using Python and modern backend frameworks.
● Own model quality, evaluation, versioning, and observability.
● Design feedback loops to continuously improve AI output quality.
● Define accuracy targets, confidence thresholds, and human review paths.
● Monitor production systems for failures, drift, and edge cases.
● Optimize inference cost, latency, and reliability.
● Instrument systems to measure usage, accuracy, and operational impact.
● Collaborate with ML Engineers and Data teams where model training or data pipelines are required.
● Ensure AI systems comply with PDPL, security, and responsible AI practices.
● Design explainable AI behaviors and human-in-the-loop workflows to build trust.
● Work closely with Finance, Operations, Sales, and other teams.
● Document system behavior, trade-offs, and limitations clearly.
● Apply AI tools to improve software delivery efficiency, quality, and reliability where there is clear impact.
● Partner with Engineering to evaluate and apply targeted AI-assisted tooling across the SDLC.