Senior Technical Lead

Job description

Key Responsibilities: Pipeline Engineering & Automation: Design, automate, and maintain robust ML evaluation pipelines (utilizing concepts similar to Apache Airflow, Kubeflow, or MLflow) for continuous, post-production model evaluation. Agentic Evaluation Infrastructure: Configure isolated/hermetic agent environments (using container orchestration principles similar to Kubernetes or Docker), integrating tools for synthetic data generation, and setting up simulated execution traces. Continuous Monitoring & Observability: Develop and maintain quality dashboards and leaderboards (similar to Grafana, Datadog, or ELK stack). Set up configurable alerting based on specific metric thresholds (Accuracy, AUC, Trajectory Exact Match) to proactively identify model degradation. Privacy & Security Implementation: Enforce strict Data Isolation Models for sensitive data. Implement strict Identity and Access Management (IAM), Role-Based Access Control (RBAC), and adhere to rigorous data retention and deletion policies (aligned with Zero Trust and strict compliance standards). Production Support: Participate in operational support to triage incidents, mitigate production issues, manage traffic rollouts, and ensure the high reliability of distributed evaluation services. Cross-Functional Collaboration: Partner with downstream client teams to onboard new AI agents (built on frameworks similar to LangChain or LlamaIndex), ingest datasets from distributed SQL or messaging systems (like Apache Kafka), and standardize model evaluation processes. Minimum Qualifications: Bachelor's degree in Computer Science, Software Engineering, or a related technical field (or equivalent practical experience).Experience in MLOps, DevOps, or Data Engineering, focusing on the ML lifecycle (continuous training, model deployment, and continuous monitoring).Proficiency in programming languages such as Python or Go.Experience working with large-scale production deployment, container orchestration (e.g., Kubernetes, Docker), and CI/CD pipelines (e.g., Jenkins, GitLab CI). Preferred Qualifications: Familiarity with Large Language Models (LLMs) and open-source Agentic AI frameworks (e.g., LangChain, AutoGen, LlamaIndex).Knowledge of standard ML evaluation metrics for Generative and Classification models (e.g., BLEURT, ROUGE, Precision/Recall, AUC).Experience managing highly sensitive data (e.g., PII/PHI) and implementing strict access control and encryption mechanisms.Strong analytical and debugging skills, with the ability to troubleshoot complex, distributed ML systems and infrastructure dependencies. Key Responsibilities Key Responsibilities: Pipeline Engineering & Automation: Design, automate, and maintain robust ML evaluation pipelines (utilizing concepts similar to Apache Airflow, Kubeflow, or MLflow) for continuous, post-production model evaluation. Agentic Evaluation Infrastructure: Configure isolated/hermetic agent environments (using container orchestration principles similar to Kubernetes or Docker), integrating tools for synthetic data generation, and setting up simulated execution traces. Continuous Monitoring & Observability: Develop and maintain quality dashboards and leaderboards (similar to Grafana, Datadog, or ELK stack). Set up configurable alerting based on specific metric thresholds (Accuracy, AUC, Trajectory Exact Match) to proactively identify model degradation. Privacy & Security Implementation: Enforce strict Data Isolation Models for sensitive data. Implement strict Identity and Access Management (IAM), Role-Based Access Control (RBAC), and adhere to rigorous data retention and deletion policies (aligned with Zero Trust and strict compliance standards). Production Support: Participate in operational support to triage incidents, mitigate production issues, manage traffic rollouts, and ensure the high reliability of distributed evaluation services. Cross-Functional Collaboration: Partner with downstream client teams to onboard new AI agents (built on frameworks similar to LangChain or LlamaIndex), ingest datasets from distributed SQL or messaging systems (like Apache Kafka), and standardize model evaluation processes. Minimum Qualifications: Bachelor's degree in Computer Science, Software Engineering, or a related technical field (or equivalent practical experience).Experience in MLOps, DevOps, or Data Engineering, focusing on the ML lifecycle (continuous training, model deployment, and continuous monitoring).Proficiency in programming languages such as Python or Go.Experience working with large-scale production deployment, container orchestration (e.g., Kubernetes, Docker), and CI/CD pipelines (e.g., Jenkins, GitLab CI). Preferred Qualifications: Familiarity with Large Language Models (LLMs) and open-source Agentic AI frameworks (e.g., LangChain, AutoGen, LlamaIndex).Knowledge of standard ML evaluation metrics for Generative and Classification models (e.g., BLEURT, ROUGE, Precision/Recall, AUC).Experience managing highly sensitive data (e.g., PII/PHI) and implementing strict access control and encryption mechanisms.Strong analytical and debugging skills, with the ability to troubleshoot complex, distributed ML systems and infrastructure dependencies. Skill Requirements Other Requirements Maximum Salary (US): Minimum Salary (US): Apply now Information at a Glance Why HCLTech? At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first. HCLTech is a global technology company, home to more than 223,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending June 2026 totaled $14.8 billion. Compensation and Benefits A candidate’s pay within the range will depend on their skills, experience, education, and other factors permitted by law. This role may also be eligible for performance-based bonuses subject to company policies. In addition, this role is eligible for the following benefits subject to company policies: medical, dental, vision, pharmacy, life, accidental death & dismemberment, and disability insurance; employee assistance program; 401(k) retirement plan; 10 days of paid time off per year (some positions are eligible for need-based leave with no designated number of leave days per year); and 10 paid holidays per year. Disclaimer Privacy Statement Terms of use Sitemap Raise a Grievance We use cookies on our site. Please read more about cookies policy here. Opens in a new tab. Opens in a new tab. Opens in a new tab. Opens in a new tab. Opens in a new tab. Opens in a new tab. Copyright © 2026 HCL Technologies Limited Cookie Consent Manager When you visit any website, it may store or retrieve information on your browser, mostly in the form of cookies. Because we respect your right to privacy, you can choose not to allow some types of cookies. 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