
Bharat testing
Weekday AI • Bengaluru, Karnataka, India
Role & seniority
- Engineering Manager / Tech Lead for ML-enabled products (leadership with hands-on depth)
Stack/tools
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Machine learning concepts and model lifecycle
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Software engineering best practices, system design, modern development stacks
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Agile practices (sprint planning, delivery tracking)
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Collaboration with product, design, data, and analytics teams
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Production deployment and operationalization of models
Top 3 responsibilities
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Lead and grow a high-performing ML-focused engineering team; set technical direction and best practices
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Oversee design, development, deployment, and operation of ML models and supporting infrastructure
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Partner with product managers to define requirements, success metrics, and delivery timelines; translate problems into technical plans
Must-have skills
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Proven Engineering Manager or Tech Lead experience with production-grade systems
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Strong ML foundation, model lifecycle management, and data-driven product development
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Ability to influence product direction through technical insight; close collaboration with product managers
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Team-building and mentoring, software engineering excellence, system design
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Clear communication to non-technical stakeholders; product-minded with value delivery focus
Nice-to-haves
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Experience building and scaling teams; mentoring engineers
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Comfort in fast-paced environments with evolving requirements and high ownership
Location & work type
Location: not specified
Work type: full-time
Full Description
This role is for one of the Weekday's clients
JobType: full-time In this role, you will own the end-to-end execution of machine learning–driven products, from ideation and experimentation to production deployment and continuous improvement. You will work closely with cross-functional stakeholders—including product, design, data, and business teams—to ensure that engineering solutions align with product goals and customer needs. As a leader, you will mentor engineers, set best practices, and foster a culture of innovation, accountability, and high performance. This role requires a strong balance of people management, hands-on technical depth, and product thinking. Key Responsibilities Lead, mentor, and grow a high-performing engineering team focused on machine learning–enabled products. Drive the technical roadmap in alignment with product vision, business priorities, and long-term scalability. Oversee the design, development, and deployment of machine learning models and supporting infrastructure. Partner closely with product managers to define requirements, success metrics, and delivery timelines. Ensure high standards of software engineering, including code quality, testing, documentation, and system reliability. Translate ambiguous business problems into clear technical solutions and execution plans. Review architectures and designs to ensure performance, security, and scalability. Establish and improve engineering processes, including agile practices, sprint planning, and delivery tracking. Collaborate with data science and analytics teams to operationalize models and measure real-world impact. Act as a bridge between technical teams and leadership, clearly communicating progress, risks, and trade-offs. What Makes You a Great Fit Proven experience as an Engineering Manager or Tech Lead, with ownership of complex, production-grade systems. Strong foundation in machine learning concepts, model lifecycle management, and data-driven product development. Demonstrated ability to work closely with product managers and influence product direction through technical insight. Experience building and scaling teams, with a passion for mentoring and developing engineers. Solid understanding of software engineering best practices, system design, and modern development stacks. Ability to balance strategic thinking with hands-on problem-solving when needed. Comfortable operating in fast-paced environments with evolving requirements and high ownership. Excellent communication skills, with the ability to explain complex technical ideas to non-technical stakeholders. A product mindset—focused not just on building systems, but on delivering measurable value to users and the business.