Tech Lead – Logistics Systems

Responsibilities

• 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 the 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 a related field.
• Advanced degrees (MSc/PhD) in AI/ML or Applied Data Science are a plus.
• Experience in logistics solutions or related fields (preferred).
• Strong communication skills, able to explain complex ML workflows and architectural decisions to technical and business stakeholders.
• Data‑driven decision‑making and ability to justify improvements using metrics.
• Balanced leadership mindset—capable of delegating and performing deep technical work.
• Comfortable navigating ambiguity and aligning stakeholders in AI‑driven product contexts.
• Expert‑level Python skills, including 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.
• Experience with model‑serving technologies (Torch Serve, MLflow, Sage Maker, Vertex AI, etc.).
• Experience with vector databases, feature stores, and embedding‑based search.
• 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, relational and No
  
  SQL databases.
• Ability to design caching, queuing, event‑driven architectures supporting ML workflows.
• Establish testing standards for data validation, model correctness, API reliability, and system performance.
• Drive observability across AI services and ensure robust monitoring coverage.