Technical Lead ML Engineer — HCL

Leading human-in-the-loop ML systems and enterprise-grade MLOps automation

Organization HCL
Role Technical Lead, Machine Learning Engineering
Focus Human-in-the-Loop ML, Model Serving, DevSecOps
Stack Label Studio, FastAPI, MLflow, Seldon, Trivy

Overview

Served as Technical Lead ML Engineer at HCL, designing and delivering human-in-the-loop machine learning systems and automated deployment pipelines. The work focused on transforming experimental ML workflows into reliable, secure, and scalable production platforms across multiple enterprise use cases.

Key Responsibilities

Human-in-the-Loop Architecture: Designed and implemented end-to-end feedback systems enabling continuous model improvement through user annotations, reducing manual data labeling effort by approximately 85 percent.
Annotation Platform Integration: Led the integration of Label Studio with a FastAPI backend, creating streamlined annotation workflows that processed over 50,000 data points per week for supervised training and evaluation.
MLOps Pipeline Automation: Architected fully automated deployment pipelines using MLflow, reducing model deployment time from more than two hours to under fifteen minutes while maintaining a 99.9 percent deployment success rate.
Enterprise Model Serving: Implemented production-grade model serving using Seldon, enabling scalable inference across multiple environments with built-in support for A and B testing and canary deployments.
Security & Compliance: Integrated Trivy-based container scanning into CI/CD pipelines, establishing DevSecOps practices that identified and mitigated approximately 90 percent of potential security vulnerabilities prior to production.
Technical Leadership: Led and mentored cross-functional teams of eight or more engineers, driving technical excellence through architecture reviews, code reviews, and structured knowledge sharing, resulting in a 40 percent improvement in team delivery velocity.

Technical Achievements

Feedback Systems: Built comprehensive human-in-the-loop pipelines combining Label Studio and FastAPI for continuous data collection and model improvement.
Deployment Automation: Established automated deployment workflows that reduced manual intervention by approximately 80 percent.
Experiment Tracking: Implemented MLflow-based experiment tracking and model versioning for reproducible experimentation and traceable production releases.
Secure Delivery: Embedded Trivy security scanning into CI/CD pipelines to ensure secure, compliant production deployments.
Scalable Serving: Deployed Seldon-based model serving infrastructure supporting scalable inference across multiple production environments.

Impact

Transformed machine learning deployment processes from manual, error-prone workflows into fully automated, secure, and repeatable systems. This enabled faster iteration cycles, higher production reliability, and stronger compliance guarantees across enterprise ML initiatives.

Outcome

This role established a robust foundation for enterprise-grade machine learning delivery at HCL, demonstrating how human-in-the-loop design, disciplined MLOps, and DevSecOps practices can scale ML systems reliably in complex organizational environments.