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Weekday, Inc.

Engineering Manager

Posted 15 Days Ago
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In-Office
Delhi, Connaught Place, New Delhi, Delhi, IND
Mid level
In-Office
Delhi, Connaught Place, New Delhi, Delhi, IND
Mid level
Lead and grow engineering teams building production-grade ML systems. Own design, deployment, MLOps, architecture, CI/CD, and cloud infrastructure while collaborating with Product and Data Science to deliver scalable AI solutions and drive engineering best practices.
The summary above was generated by AI

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟭𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟭𝟬𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟭𝟬-𝟭𝟬𝟬 𝗟𝗣𝗔)

Experience: 3+ yrs

Location: India

Job Type: Full-time

We are seeking an experienced Engineering Manager to lead high-performing engineering teams focused on building scalable, production-grade Machine Learning solutions. This role is ideal for professionals who combine strong technical expertise in machine learning with proven leadership skills to drive engineering excellence, mentor teams, and deliver innovative AI-powered products.

As an Engineering Manager, you will oversee the design, development, deployment, and optimization of machine learning systems while collaborating closely with Product, Data Science, Platform Engineering, and Business stakeholders. You will be responsible for establishing engineering best practices, enabling technical innovation, and ensuring the successful delivery of reliable, scalable, and impactful ML solutions. This role requires a balance of technical depth, people leadership, and strategic thinking to align engineering efforts with business objectives.


RequirementsKey Responsibilities
  • Lead, mentor, and grow engineering teams building machine learning products and AI-driven applications.
  • Drive the design, development, deployment, and maintenance of scalable machine learning systems and production pipelines.
  • Collaborate with Data Scientists, ML Engineers, Product Managers, and cross-functional teams to translate business requirements into technical solutions.
  • Establish engineering standards, development processes, and best practices for software quality, machine learning operations, and system reliability.
  • Oversee project planning, resource allocation, sprint execution, and technical delivery to ensure successful outcomes.
  • Guide architectural decisions for ML platforms, data pipelines, model serving, and cloud-based infrastructure.
  • Improve model deployment, monitoring, performance optimization, and lifecycle management using MLOps principles.
  • Conduct code reviews, technical design discussions, and mentoring sessions to elevate engineering quality and team capabilities.
  • Track engineering metrics, identify risks, and drive continuous improvements in productivity, scalability, and operational efficiency.
  • Foster a culture of innovation, collaboration, accountability, and continuous learning across engineering teams.
What Makes You a Great Fit
  • 3+ years of experience in software engineering with significant exposure to Machine Learning systems and engineering leadership.
  • Proven experience managing engineering teams while delivering production-grade AI or machine learning solutions.
  • Strong understanding of machine learning workflows, model deployment, MLOps, data engineering, and cloud-native architectures.
  • Experience working with Python, ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar technologies.
  • Familiarity with cloud platforms, containerization, CI/CD pipelines, Kubernetes, and scalable infrastructure for ML workloads.
  • Strong knowledge of software architecture, distributed systems, API development, and engineering best practices.
  • Excellent leadership, mentoring, stakeholder management, and cross-functional collaboration skills.
  • Strong analytical thinking, problem-solving abilities, and a data-driven approach to technical decision-making.
  • Ability to balance technical execution with strategic planning, people management, and business priorities.
  • Passion for building high-performing engineering teams, driving innovation, and delivering impactful machine learning products at scale.

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