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Remote mid-level ML role with moderate brand and broad skill requirements increases applicant competition.
Strong ML production and MLOps requirements make this role highly domain-specific and less industry-transferable.
Explicit 5+ years requirement plus mandatory production ML, LLM, cloud, MLOps and big-data tech stack.
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Own end-to-end machine learning lifecycle: research, development, deployment, scaling, and continuous optimization in production environments.
Design and implement ML algorithms solving key business problems like visibility, prediction, demand forecasting, and freight audit with scalable, reliable infrastructure.
Collaborate closely with product and customer teams to translate loosely defined problems into deployed ML features, maintaining control over model specs and priorities.
Bachelor's, Master's, or PhD in Computer Science, Engineering, or related field.
5+ years experience building, deploying, and scaling ML models in production, including hands-on experience with productionizing LLM-based systems.
Proficient in Python, SQL, cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), real-time data processing, and big data technologies like Spark and Kafka.
Work Experience Required: At least 5 years in ML production environments. Notice period: Not explicitly mentioned in the JD.
Experienced in fast-paced, startup or product-based company environments, comfortable with early-stage technical product development and independent ownership.
Skilled in practical, reliable, cost-aware ML engineering with a strong focus on the full ML product lifecycle and continuous monitoring in production.
Capable of working autonomously as a key member of a small data science team, taking initiative on loosely defined problems and delivering impactful ML solutions end to end.