Job specializations:
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Software Development
Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Engineering
Job Description & How to Apply Below
- Lead a cross-functional engineering team focused on Python applications, AI/ML services, and data-driven platforms.
- Mentor Senior, Middle, and Junior Engineers, ensuring alignment with engineering standards and ML project requirements.
- Set short-, mid-, and long-term goals for the team, continuously supporting skill growth in software engineering and ML system design.
- Coordinate with Product, Data Science, QA, Dev Ops, and Platform Leads to ensure the successful delivery of AI/ML initiatives.
- Drive end-to-end architecture and design for AI/ML applications, model-serving infrastructure, and Python backend services.
- Oversee architectural decisions for scalable model deployment (REST APIs, streaming pipelines, batch systems), ensuring performance, reliability, and maintainability.
- Define standards for feature engineering pipelines, data ingestion flows, and integration with MLOps platforms.
- Break down complex AI/ML initiatives into actionable engineering tasks and clear technical roadmaps.
- Collaborate with Data Scientists to product ionize ML models, ensuring reproducibility, versioning, and monitoring.
- Lead implementation of model-serving frameworks, vector databases, data processing jobs, and inference optimization strategies.
- Ensure adherence to best practices in experiment tracking, model evaluation, and A/B testing for ML features.
- Improve development workflows, CI/CD pipelines, and ML lifecycle automation.
- Promote coding standards, technical documentation, and reduction of technical debt across AI/ML projects.
- Introduce new tools and technologies that enhance model performance, data quality, and engineering productivity.
- Participate in hiring for both backend and ML-focused engineering roles.
- Facilitate knowledge-sharing sessions around ML system architecture, Python best practices, and cloud-native development.
- Strengthen team culture through collaboration, mentorship, and proactive communication.
- Oversee reliability of AI/ML services, including model drift detection, data quality monitoring, and performance metrics.
- Coordinate incident response, root‑cause analysis, and cross‑team escalations for production ML systems.
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
- Experience in logistics solutions or related fields.
- Advanced degrees (MSc/PhD) in AI/ML or Applied Data Science are a plus.
- Strong communication skills, able to explain complex ML workflows and architectural decisions to technical and business stakeholders.
- Data‑driven decision‑making and the ability to justify improvements using metrics.
- Balanced leadership mindset—capable both of delegating and of hands‑on deep technical work.
- Comfortable navigating ambiguity and aligning stakeholders in AI‑driven product contexts.
- Expert‑level Python developer with strong understanding of backend frameworks (FastAPI, Flask, Django) and microservices patterns.
- Strong grounding in algorithms, distributed systems, and scalable backend architecture.
- Experience deploying ML models to production (batch, real‑time, streaming).
- Knowledge of ML frameworks such as Tensor Flow, PyTorch, Scikit‑learn, and model‑serving technologies (like Torch Serve, MLflow, Sage Maker, Vertex AI, etc.).
- Experience with vector databases, feature stores, and embedding‑based search is a strong plus.
- Hands‑on experience with CI/CD for ML systems, containerization (Docker), orchestration, and cloud environments (AWS/GCP).
- Familiarity with monitoring tools for ML pipelines: logging, metrics, tracing, model drift detection.
- Strong understanding of data pipelines, ETL/ELT workflows, and both relational and No
SQL databases. - Ability to design caching, queuing, and event‑driven architectures supporting ML workflows.
- Establishes testing standards for data…
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