TBTanishk Bhardwaj

EXPERIENCE

Learning by building in real operational environments.

Experience across logistics, industrial systems, business optimization, and telecommunications in India and Bahrain.

  1. Diagnostics

  2. Business analysis

  3. Predictive systems

  4. Operational automation

  1. February 2026 — Present

    01

    Computer Vision Intern

    Safexpress Pvt. Ltd.

    Gurugram, India

    Applied AI, analytics, and automation work for real logistics workflows.

    Purpose
    Computer vision, analytics, and automation for logistics workflows.
    Ownership
    Contributed image-decoding, conversation-analysis, and ongoing SOP proof-of-concept work.
    Evidence
    1,000-image validation set · ~15,000 conversations analyzed · 18 confidential POC scenarios

    Stack Python · OpenCV · Snowflake · SQL · Computer vision

    What I learned

    I applied computer vision and analytics to practical logistics workflows.

    • Built a lightweight Code 128 waybill-decoding workflow for difficult, damaged, or low-quality images.
    • Analyzed approximately 15,000 historical BI Copilot conversations to identify intent patterns, failure points, and improvement opportunities.
    • Contributing to an ongoing computer-vision SOP compliance proof of concept for confidential logistics scenarios.

    Confidential logistics details are generalized, and the CCTV proof of concept is not described as a finished production deployment.

  2. December 2024 - January 2025

    02

    Electrical & Instrumentation Intern

    Bahrain Steel

    Hidd, Bahrain

    Industrial telemetry, predictive maintenance, and stakeholder-facing technical analysis.

    Purpose
    Predictive-maintenance analysis using industrial motor telemetry.
    Ownership
    Engineered rolling-window features and compared model approaches for a normalized target.
    Evidence
    100k+ telemetry records · ~200 sensors · ~10 motor systems

    Stack Python · pandas · scikit-learn · XGBoost

    What I learned

    I moved from analysis into predictive modeling with real industrial telemetry.

    • Built a predictive-maintenance workflow using more than 100,000 real telemetry records.
    • Worked with data from approximately 200 sensors across approximately 10 motor systems.
    • Engineered rolling-window features, compared Random Forest and XGBoost, and presented the project to internal stakeholders.
  3. May 2024 - August 2024

    03

    AI & Business Optimization Intern

    Foulath Holding

    Al Hidd, Bahrain

    Internal analytics, structured reporting, and risk-prioritization workflows.

    Purpose
    Internal analytics and business-optimization workflow support.
    Ownership
    Helped structure analytical reporting and surface risk findings for review.
    Evidence
    50+ network-risk findings surfaced for review

    Stack Analytics workflows · LLM-assisted analysis · Risk review

    What I learned

    I connected analytical workflows with internal business decisions and investigation.

    • Developed an LLM-assisted internal analytics workflow using confidential business data.
    • Helped flag suspicious patterns for team investigation and structured reporting.
    • Built an ML-assisted workflow that surfaced more than 50 network-risk findings for review.

    Internal data, reports, workflows, and architecture are kept generalized.

  4. January 2024 - February 2024

    04

    Telecommunications Technology Intern

    Viacloud W.L.L.

    Manama, Bahrain

    Telecommunications diagnostics, system performance review, and issue investigation.

    Purpose
    Telecommunications diagnostics and system-performance review.
    Ownership
    Reviewed logs, diagnostics, and module-level indicators across technical systems.
    Evidence
    5+ systems reviewed · 10+ technical issues contributed to

    Stack Logs · Diagnostics · Performance indicators

    What I learned

    I learned to inspect technical systems through logs, diagnostics, and performance indicators.

    • Reviewed logs, diagnostics, and module-level performance indicators across multiple systems.
    • Contributed to bottleneck identification and technical issue resolution.
    • Worked with engineering teams across more than five systems or modules.