Senior Technical Lead

HCLTech; San Jose (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

HCL Technologies Limited is seeking a Senior Technical Lead to design and automate ML evaluation pipelines and to build hermetic agent environments for robust post-production model assessments.

The role emphasizes advanced orchestration, monitoring dashboards, and strict data security practices. You will lead cross-functional efforts with client teams and ensure reliable, scalable evaluation services in production.

Qualifications

• Bachelor's degree or equivalent in CS/SE or related field.
• Experience in MLOps, DevOps, or Data Engineering across the ML lifecycle.
• Proficiency in Python or Go with strong debugging skills.
• Experience with large-scale production deployment, container orchestration, and CI/CD pipelines.
• Familiarity with agentic AI frameworks (LangChain, LlamaIndex) is a plus.

Responsibilities

• Design, automate, and maintain robust ML evaluation pipelines (Airflow/Kubeflow/MLflow) for continuous post-production model evaluation.
• Configure isolated/hermetic agent environments using container orchestration principles (Kubernetes/Docker).
• Develop and maintain dashboards and leaderboards; set up alerting on metrics like Accuracy, AUC, and trajectory matching.
• Enforce Data Isolation Models, IAM, RBAC, and data retention/deletion policies (Zero Trust).
• Provide Production Support to triage incidents and ensure reliability of distributed evaluation services.
• Collaborate with downstream client teams to onboard new AI agents and standardize evaluation processes.

Skills

Python

Go

MLOps

DevOps

Data Engineering

CI/CD

Education

Bachelor's degree in Computer Science, Software Engineering, or related field

Tools

Kubernetes

Docker

Jenkins

GitLab CI

Airflow/Kubeflow/MLflow

Job description

Career Opportunities: Senior Technical Lead (189843)

Requisition ID189843-Posted11/05/2026

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.