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Maneesh Ujji
Junior Machine Learning & Data Science professional specializing in AI agents and applied ML
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IT Analyst/research background with hands-on experience deploying and hardening a multi-agent AI support/triage system (ticket ingestion + knowledge-base retrieval) with strong emphasis on reliability and observability. Has debugged real production issues spanning backend services and network latency (sync failures/partial writes) and is comfortable in Linux environments; also has academic exposure to robotics simulation and ROS2.
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Key Strengths
- Hardening multi-agent AI workflows with strict schemas, validation, retries, and escalation to prevent silent failures
- Productionizing services via containerization, dependency locking, and health checks to reduce environment-related issues
- Strong observability mindset (logging/health checks) to make systems debuggable and supportable
- End-to-end troubleshooting across application, backend services, and network layers (e.g., latency-induced partial writes)
- Customer-facing/on-site support: translating user issues into technical symptoms and feeding edge cases back into product improvements
- Building configurable Python systems to support customer-specific routing rules and response formats without destabilizing core code
Reference Highlights
- Strong ability to deploy and support complex systems in production
- Exceptionally fast learner
- Develops deep, practical understanding of new technologies
- Explains complex concepts clearly and intuitively
- Methodical debugger
- Highly organized; enables smooth team handoffs via logging
- Strong attention to detail
- Persistent in finding root causes
- Communicates findings clearly to the team
- Calm and focused under pressure
- De-escalates tense customer situations
- Strong customer communication; makes users feel supported
- Adaptable in fast-changing environments
- Translates operational needs into practical technical solutions
- Strong Python skills
- Comfortable with existing codebases and debugging
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