GPU Resource Efficiency: Evaluate inference workloads across different GPU generations and configurations. Improve resource utilization while balancing latency, throughput, cost, and operational constraints, including GPU sharing or partitioning where appropriate.
Transparent culture: We publicly share the company's information to all, every member can read and feedback, and become a part of participating in the proposal.
Select the appropriate architecture for each use case rather than applying multi-agent patterns by default:Deterministic workflow and state-machine architectures for repeatable, high-confidence operational processes.Planner-executor architectures for decomposing complex investigation, planning, and remediation tasks.Supervisor-worker or manager-worker architectures for coordinating specialist domain agents.Router architectures for selecting the right model, tool, knowledge source, workflow, or specialist agent.Reflection, critic, verifier, or judge patterns for quality assurance, validation, and safety checks.Event-driven architectures for responding to telemetry anomalies, workload failures, scheduler events, benchmark regressions, and operational alerts.Human-in-the-loop architectures for high-impact recommendations, privileged actions, or changes to production environments.
Embed Responsible AI: Build evaluation frameworks, guardrails and monitoring capabilities to manage AI reliability, hallucinations, security and data privacy, incorporating human-in-the-loop review, auditability and appropriate escalation pathways.
ML Pipeline Contribution: Package models in Docker; contribute to CI/CD scripts under guidance; run inference tests and support deployment validation in product development environments.
Develop intelligent summarization and information extraction pipelines that process thousands of scraped news articles, press releases, and open-source intelligence into concise, actionable market intelligence reports, leveraging techniques such as intelligent chunking, semantic filtering (embeddings + k-NN), map-reduce patterns, TF-IDF augmentation, and agentic orchestration.
· Emerging-Risk Agent Development: Develop AI agents that analyse approved information sourcesto identify, generate and prioritise evidence-linked emerging fraud and abuse risks for officer review.
Experience designing and implementing lakehouse or data-lake pipelines using Databricks, Delta Lake and AWSS3, including bronze, silver and gold data-layer patterns.
Hands-on experience building and deploying ML/AI applications, including at least one LLM-enabled or agentic AI solution beyond proof-of-concept stage.
