Tech Lead – Logistics Systems

Overview

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, DevOps, and Platform Leads to ensure the successful delivery of AI/ML initiatives.

Architecture & System Design

• 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.

AI/ML System Delivery

• Collaborate with Data Scientists to productionize 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.

Process Improvement & Engineering Excellence

• 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.

Recruitment, Culture & Knowledge Sharing

• 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.

Incident Management & Reliability

• 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.

🧙‍♂️ The Magic You Bring

• 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.

Python & Backend Engineering

• 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.

AI/ML Engineering

• Experience deploying ML models to production (batch, real-time, streaming).
• Knowledge of ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and model-serving technologies (like TorchServe, MLflow, SageMaker, Vertex AI, etc.).
• Experience with vector databases, feature stores, and embedding-based search is a strong plus.

MLOps & Cloud

• 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.

Data, Storage & Integration

• Strong understanding of data pipelines, ETL/ELT workflows, and both relational and NoSQL databases.
• Ability to design caching, queuing, and event-driven architectures supporting ML workflows.

Quality & Reliability

• Establishes testing standards for data validation, model correctness, API reliability, and system performance.
• Drives observability across AI services and ensures robust monitoring coverage.

Inside Snoonu’s Universe

The Dream We’re Chasing

The Quest We’re On

👥 Be Customer Obsessed

👨‍💼 It's All About People

Perks & Worklife Magic At Snoonu

Builder’s Playground

Flexible Time Off

Agile Everything

Great Place to Work® Certified

Beyond the Code: Giving Back Matters

Diversity Isn’t Just a Buzzword

Let’s Build the Future Together