Senior Machine Learning Engineer – MLOps

🕒 September 10

🇮🇳 India – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 20%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Dynatron Software, Inc.

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 1999

💼 Consulting

📣 Marketing

📦 Logistics

Consulting • Marketing • Logistics

Dynatron Software, Inc. is a company that combines advanced analytics software with expert coaching to help automotive service departments maximize their revenue opportunities. Their product suite includes various solutions like PriceSmart for optimizing labor and part pricing, FileSmart for enhancing warranty labor rates and parts markup, SellSmart for increasing service sales to existing customers, and MarketSmart for boosting service department traffic through strategic marketing campaigns. With a focus on increasing profitability, Dynatron collaborates closely with dealerships to identify and capitalize on hidden revenue streams using their proprietary analytics and extensive repair order database.

📋 Description

• Design, build, and maintain deployment pipelines moving models from development through validation into production • Establish model versioning, lineage, registry, and automated promotion practices • Define repeatable production-readiness standards and deployment patterns across ML and AI workloads • Partner with Data Scientists to make model handoffs efficient, consistent, and production-ready • Own production monitoring across model performance, drift, data quality, inference health, latency, and availability • Establish alerts and operational thresholds to identify degradation before it impacts products or customers • Diagnose production failures, perform root-cause analysis, and implement durable corrective actions • Build operational practices that improve reliability as the production model portfolio grows • Deploy and support production LLM applications, including retrieval-based and agentic architectures • Build evaluation frameworks measuring quality, reliability, and performance of generative AI capabilities • Monitor token consumption, inference costs, and cost per interaction • Implement controls around model access, usage, safety, and production behavior • Design and operate infrastructure supporting model training, validation, and retraining • Build automated retraining pipelines triggered by performance, data, or business conditions • Ensure training environments and workflows are reproducible, scalable, and observable • Partner with Data Engineering and Data Science to ensure reliable data movement throughout the ML lifecycle • Implement model access controls, auditability, lineage, and governance standards • Support model risk classification and appropriate controls based on use case and business impact • Produce documentation and technical evidence supporting security, compliance, and internal governance requirements • Respond to incidents, troubleshoot failures, and coordinate resolution across teams • Build runbooks and operational procedures that reduce dependence on tribal knowledge • Identify recurring operational issues and automate them where practical

🎯 Requirements

• 6+ years of experience in software engineering, data engineering, machine learning engineering, or a related technical discipline • 3+ years of hands-on experience deploying and operating AI/ML systems in production • Demonstrated experience supporting both traditional machine learning and LLM-based workloads in production • Strong understanding of the complete model lifecycle from development and validation through deployment, monitoring, retraining, and retirement • Production experience with LLM-powered applications and agentic frameworks • Experience with retrieval architectures, evaluation methodologies, and production monitoring for generative AI • Understanding of LLM performance, latency, token utilization, and cost-per-interaction management • Ability to establish practical operational and governance controls around generative AI systems • Deep experience with a major cloud platform and its managed AI/ML services; AWS strongly preferred • Hands-on experience with model registries, pipeline orchestration, ML CI/CD, automated retraining, and production monitoring • Strong Python engineering skills • Experience with containerization and infrastructure-as-code • Experience designing reliable, repeatable, and automated production environments • Experience operating production services with meaningful ownership for reliability and availability • Strong incident response, troubleshooting, and root-cause analysis skills • Ability to distinguish symptoms from underlying system failures and implement long-term solutions • Comfortable making sound operational decisions independently when immediate U.S.-based support may not be available • Strong written technical communication skills • Experience creating runbooks, architectural documentation, standards, and operational procedures • Proactive communication style suited to distributed, asynchronous teams • Ability to work effectively across Data Engineering, Data Science, Product, and other technical functions • Bachelor’s degree in Computer Science, Engineering, or a related technical discipline, or equivalent practical experience

🏖️ Benefits

• Competitive local benefits provided through our Employer of Record • Remote working environment • Ongoing professional development opportunities • Opportunity to work directly with U.S.-based Data and Technology teams • Meaningful ownership of production systems supporting Dynatron’s AI strategy