Tech Lead – Logistics Systems

What You’ll Get Your Hands On Team Leadership

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 (Fast API, 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 validation, model correctness, API reliability, and system…