Jobs at Dynatron Software

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.

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.

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.

Lead regional revenue and market expansion across the assigned Canadian territory. Own full sales cycle from prospecting to close, expand existing accounts, execute territory plans, engage dealership decision-makers through frequent in-person meetings, and maintain disciplined pipeline management in SugarCRM while collaborating with Sales Leadership, Marketing, and Enablement.

Lead Enterprise Client Success for Dynatron's largest customers, owning GRR/NRR, building strategy and playbooks, developing a high-performing team, driving adoption and expansion using data, and partnering cross-functionally to deliver measurable customer value and predictable revenue outcomes.

Lead design, build, and operate scalable AWS-based data pipelines and real-time ingestion for analytics and ML. Implement data modeling, automated validation, observability, and event-driven architectures; mentor engineers and collaborate with Product and ML teams to productionize feature stores and ML-ready datasets.