Role Summary :
The Head of Technology is a newly scoped leadership role designed to unify LatentView's technology function across three high-growth verticals. This is not a purely functional or delivery role — it is a business-critical leadership position that sits at the intersection of AI engineering, enterprise software, and client value creation.
The role is being created to address three imperatives:
- Establish a unified, scalable AI and engineering practice that spans CPG, Marketplace & Retail, and Industrial verticals — moving from bespoke builds to reusable, enterprise-grade platforms.
- Provide senior technical authority that talented Tech Leads, Data Engineers, and Data Scientists can rally behind — setting direction, standards, and culture.
- Build deep technology-business fluency: a leader who translates client business problems into scalable software solutions, not just technical deliverables.
Reporting directly to the Head of Delivery, the Head of Technology will own the full technology mandate for LatentView's three core verticals. This includes AI practice development, solution architecture, enterprise coding standards, security frameworks, and the productionisation of AI/analytics solutions at client scale. Equally, the role demands exemplary people leadership — building, inspiring, and retaining a multi-disciplinary technology team while serving as the primary technical voice in senior client conversations.
KEY RESPONSIBILITIES
1. AI Practice & Agentic Solution Development
- Own the strategic roadmap for LatentView's AI Practice — defining service offerings, technology choices, and capability investments across all three verticals.
- Lead the design and delivery of agentic AI solutions: autonomous agents, multi-agent orchestration systems, and LLM-powered workflows embedded in enterprise client environments.
- Evaluate and adopt frontier AI frameworks (e.g., LangGraph, AutoGen, CrewAI, OpenAI Assistants) and translate them into production-viable architectures.
- Establish and govern a model registry, prompt engineering standards, and responsible AI guardrails applicable across client engagements.
- Drive thought leadership — contribute to LatentView's external positioning through POVs, publications, and client workshops on agentic AI and applied ML.
2. Solution Architecture & Design
- Act as the chief architect for all major client engagements — owning end-to-end solution design from data ingestion to model deployment to business reporting.
- Define and enforce architecture standards: cloud-native design patterns, microservices, API-first principles, and scalable data platform architectures (Lakehouse, Medallion, etc.).
- Lead architecture review boards for key programmes, ensuring designs are production-grade, cost-optimised, and aligned with client enterprise standards.
- Build reusable accelerators, reference architectures, and IP assets that reduce time-to-value across engagements in CPG, Retail, and Industrial sectors.
- Partner with Data Science teams to architect MLOps pipelines — feature stores, model training infrastructure, model monitoring, and drift detection frameworks.
3. Enterprise-Grade Coding Practices
- Define and institutionalise LatentView's software engineering standards: code review protocols, branching strategies, CI/CD pipelines, and test automation frameworks.
- Champion engineering excellence: clean code principles, SOLID design, test-driven development, and documentation standards across all software and data engineering workstreams.
- Implement inner-source practices — enabling teams across verticals to share, reuse, and contribute to a common internal library of tools and services.
- Evaluate and standardise the technology stack (languages, frameworks, orchestration tools, cloud services) to reduce fragmentation and improve maintainability.
- Introduce developer productivity metrics (DORA metrics, cycle time, deployment frequency) and build a culture of continuous engineering improvement.
4. Enterprise Security & Privacy Standards
- Own and enforce LatentView's enterprise security posture for all client-facing solutions — covering application security, data privacy, and infrastructure hardening.
- Establish a security-by-design culture: threat modelling, vulnerability assessment, dependency scanning, and secure coding standards embedded in every delivery lifecycle.
- Lead compliance alignment for client data environments — including SOC 2 Type II, and ISO 27001 — in partnership with Legal and Compliance.
- Define privacy-preserving data practices: anonymisation, pseudonymisation, differential privacy, and data residency controls, especially critical in CPG and Industrial engagements.
- Govern AI-specific security considerations: prompt injection prevention, model access controls, data leakage risks in LLM deployments, and adversarial robustness.
5. Productionising Solutions at Scale
- Drive the organisation's maturity in taking AI and analytics solutions from prototype / POC to production-grade, enterprise-deployable systems.
- Own the platform engineering layer: container orchestration (Kubernetes), infrastructure-as-code (Terraform/CDK), observability stacks, and SRE practices.
- Define SLAs, SLOs, and operational run-books for live client solutions — ensuring reliability, performance, and scalability under production load.
- Establish a structured productionisation playbook — covering feature engineering pipelines, model serving (real-time and batch), monitoring, and human-in-the-loop workflows.
- Collaborate with Delivery and Client Success teams to ensure client environments (cloud tenants, on-prem, hybrid) are supported with robust deployment and handover processes.
6. Team Leadership & People Development
- Lead a multi-disciplinary team of Tech Leads, Senior Software Engineers, Data Engineers, and Data Scientists — providing technical mentorship and career development.
- Act as the technical north star for the team: creating clarity of direction, setting high engineering standards, and fostering a culture of curiosity, craft, and accountability.
- Attract and hire senior engineering talent; build a compelling technical brand for LatentView in India's tech ecosystem.
- Run regular architecture forums, tech talks, and learning programmes — keeping the team at the forefront of AI/ML and software engineering developments.
- Partner with Delivery leadership on resource planning, capacity management, and allocation of technical talent across verticals and client programmes.
7. Client Centricity & Business Engagement
- Serve as the senior technical voice in C-suite client conversations — translating complex technology capabilities into business outcomes and ROI narratives.
- Partner with Account and Business Development teams during pre-sales: solution scoping, effort estimation, technical proposal writing, and client presentations.
- Develop a deep understanding of the business dynamics of CPG, Marketplace & Retail, and Industrial sectors — speaking the language of the client's business, not just the technology.
- Build trusted advisor relationships with client CTOs, CDOs, and VP Engineering counterparts — influencing the direction of long-term technology partnerships.
- Participate in QBRs, steering committees, and innovation workshops with Tier-1 clients, positioning LatentView's technology vision and roadmap.
QUALIFICATIONS & EXPERIENCE
- 15+ years of progressive technology experience, with at least 5 years in a senior engineering leadership role (Engineering Director, VP Engineering, Head of Technology, or equivalent).
- Demonstrable track record of architecting and delivering enterprise-scale AI/ML or data solutions in a services or product company context.
- Deep hands-on expertise in at least one major cloud platform (AWS, Azure, or GCP) and associated data and AI services.
- Proven experience leading multi-disciplinary engineering teams (software engineers, data engineers, data scientists) of 30+ people.
- Strong knowledge of modern software engineering practices: CI/CD, DevOps, infrastructure-as-code, containerisation, and API design.
- Hands-on experience with LLMs, generative AI frameworks, and agentic architectures — from design through to production deployment.
- Experience setting enterprise security standards and navigating compliance frameworks (SOC 2, GDPR, or equivalent).
- Excellent communication skills — able to engage credibly with both engineering teams and C-level business stakeholders.
Preferred
- Domain experience in one or more of CPG, Retail/E-commerce/Marketplace, or Industrial/Manufacturing sectors.
- Experience in a global data analytics or consulting firm with exposure to multi-client, multi-vertical delivery models.
- Exposure to pre-sales and solution commercialisation — proposal writing, effort scoping, and technical storytelling for business audiences.
- Advanced degree (M.Tech, MS) in Computer Science, Engineering, or a related quantitative field.
- Published thought leadership, conference presentations, or open-source contributions in AI/ML engineering.
