Head of Engineering

Role: Head - Engineering

Type: Full-time

Location: Delhi NCR / Bhopal (Remote)

Experience: 15+ YOE

Reports to: Head - Implementation

Manages: AI, Full Stack & Data Analytics Engineering Teams

ABOUT SUTRA.AI

Sutra.AI is a complete ‘Data to Value’ AI business transformation platform designed specifically for Business Builders. Unlike other platforms that cater to AI Builders or large enterprises with substantial budgets, Sutra.AI is purpose-built for mid-market businesses without the need of AI expertise. Developed over 4 years, Sutra.AI is a patent-pending fully orchestrated AI SaaS platform. It identifies and executes AI use cases to enhance operational efficiency, customer experience, revenue growth, profitability, innovation, and risk reduction.

WHY THIS ROLE MATTERS

Engineering execution is where Sutra.AI's promises become working software in front of customers. This role owns how our AI, Full-Stack, and Data Analytics engineering teams build, ship, and stabilise across multiple concurrent engagements. By driving predictable execution, lifting first-time quality, and keeping defect rates and turn-around times low, you directly raise customer confidence in delivery and accelerate platform adoption. This is an engineering leadership role, not a program office: depth in how systems are built, integrated, and run matters as much as the rhythm of getting them done.

WHAT YOU'LL DO

1. Engineering Execution & Delivery Quality

• Own day-to-day engineering execution across AI, Full-Stack, and Data Analytics teams, ensuring 95%+ of build milestones are met on time.
• Drive first-time engineering quality, reducing defect rates and production issues across deployments.
• Hold the bar on application performance, stability, and uptime in delivered solutions.
• Improve turn-around time on customer deliverables and engineering requests, and reduce quality-driven escalations.

2. Technical Depth & Solution Engineering

• Visualise and model the relationships between operational systems, workflows, and data sources to shape sound engineering plans.
• Translate solution and operational requirements into concrete engineering approaches and build sequences.
• Proactively challenge architectural gaps, evaluate technical feasibility, and make pragmatic build-versus-configure decisions.
• Oversee data pipelines, workflow integration, and integration dependencies across the stack.

3. Engineering Team Leadership & Capacity

• Lead and grow cross-functional engineering teams (AI, Full-Stack, Data Analytics Engineers), scaling concurrent execution capacity.
• Establish clear, data-driven visibility into engineering resource allocation and utilisation across engagements.
• Handle engineering staffing, prioritisation, and technical skill development so the team keeps pace with demand.
• Build engineering standards, code and review discipline, and repeatable execution practices.

4. Risk Management & Platform Alignment

• Identify engineering and operational risks early, design mitigation plans, and resolve them with stakeholders rather than only reporting them.
• Drive platform adoption, ICP alignment, and AI adoption principles through how solutions are engineered.
• Partner closely with Platform, Solutioning, Data, and Implementation teams to keep technical execution aligned to customer value.

REQUIREMENTS

Must-Have

• Experience: 15+ years leading cross-functional engineering teams (AI, full-stack, and data / analytics).
• Environment: Direct experience working within startups or fast-growth environments.
• Stakeholder Management: Led technical builds and implementations involving IT, engineering, AI, and business stakeholders.
• Team Leadership: Managed the day-to-day operations of cross-functional engineering / execution teams.
• Data & AI Literacy: Solid understanding of AI concepts, data pipelines, workflow integration, and operational AI constraints.
• Domain Exposure: Professional exposure to SaaS or AI technology landscapes.

Preferred

• Experience leading AI Transformation Delivery programs.
• Exposure to mid-market customers. Enterprise experience is a plus, not a requirement.

CORE COMPETENCIES

Technical & Functional Competencies

• Engineering Execution & Quality: Owning engineering throughput, milestones, and first-time quality across concurrent builds.
• Technical & Architecture Depth: Requirement translation, identifying architectural gaps, feasibility checking, and integration dependency tracking.
• Data & AI Literacy: Working understanding of AI concepts, data pipelines, workflow integration, and operational AI constraints.
• Resource & Capacity Leadership: Engineering staffing, allocation tracking, utilisation, and optimisation.
• Risk Management: Proactive risk identification, mitigation design, and cross-functional resolution.
• Operational Adaptability: Workflow mapping, systems-landscape assessment, and execution flexibility across customers and industries.