Dynatron Software

HQ
Richardson
Total Offices: 2
121 Total Employees
Year Founded: 1997

Dynatron Software Benefits Overview

Compensation + Benefits

Offers 401(K)

Offers supplemental life insurance

Offers life insurance

Offers accidental death & dismemberment insurance

Offers company equity

Offers performance bonuses

Offers dental insurance

Offers vision insurance

Offers health insurance

Offers Health Savings Account (HSA)

Offers generous parental leave

Career Growth + Development

Provides customized development tracks

Work-Life Balance + Wellbeing

Offers generous PTO

Provides bereavement leave

Provides paid holidays

Company Culture

Offers a remote work program

Analytics
Own the production lifecycle for traditional ML and generative AI systems, including deployment pipelines, model registries, monitoring, retraining, governance, and incident response. Build reliable AWS-based infrastructure for LLM and agentic applications, establish observability and cost controls, and create operational standards, runbooks, and documentation. Partner with U.S.-based Data Science, Data Engineering, and Product teams while independently managing production workloads during India operating hours.
26 Days AgoSaved
In-Office
Richardson, TX, USA
Analytics
Lead production-focused AI and machine learning development across classification, prediction, generative AI, LLM, and agentic use cases. Own modeling, evaluation, deployment, monitoring, and lifecycle improvement for customer-facing capabilities. Partner with Product, Engineering, Data Engineering, and MLOps to assess feasibility, prioritize opportunities, establish evaluation standards, and translate complex automotive data into reliable, scalable AI products. The role also provides technical leadership and product judgment without direct people-management responsibility.
One Month AgoSaved
Remote
India
Analytics
Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.