V
Director of Data & AI Engineering
V
Director of Data & AI Engineering
vidpro tech & hr solutions private limited- Posted 16 hours ago
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Job Description
About The Opportunity
We operate in the Enterprise Data & Artificial Intelligence sector, delivering production-grade ML, MLOps, and scalable data-platform solutions to enterprise customers across industries. This is a senior engineering leadership role for a hybrid-based team in India, responsible for building high-performance Data & AI engineering capabilities that convert advanced models into reliable, secure, and cost-effective production services.
Role & Responsibilities
Must-Have
We welcome senior engineering leaders who are passionate about turning ML research into reliable, production-quality systems. Apply to help shape the next generation of enterprise Data & AI platforms.
Skills: cloud,enterprise,data engineering,llm,gen ai,python,rag,ml,data,artificial intelligence,platforms,data science,mlops,agentic ai,genai
We operate in the Enterprise Data & Artificial Intelligence sector, delivering production-grade ML, MLOps, and scalable data-platform solutions to enterprise customers across industries. This is a senior engineering leadership role for a hybrid-based team in India, responsible for building high-performance Data & AI engineering capabilities that convert advanced models into reliable, secure, and cost-effective production services.
Role & Responsibilities
- Define and execute the Data & AI engineering strategy and roadmap—align architecture, tooling, and delivery to business outcomes and go-to-market priorities.
- Lead, hire, and scale cross-functional teams (ML engineers, data engineers, MLOps, SRE) to deliver production-grade ML and data platforms.
- Architect and operationalize end-to-end ML systems: data ingestion, feature stores, training pipelines, model registry, serving, monitoring, and automated retraining.
- Establish engineering best practices: CI/CD for ML, infrastructure as code, observability, security, cost controls, and incident management.
- Partner with Product, Customer Success, and enterprise clients to translate use-cases into measurable KPIs and production SLAs; drive prioritization and delivery cadence.
- Manage vendor relationships, procurement, budgets for cloud and third-party AI tooling; mentor senior managers and build a culture of technical excellence and ownership.
Must-Have
- Proven track record leading engineering teams that built and operated production ML or data platforms at scale.
- Deep technical understanding of ML lifecycle and MLOps—model training, deployment, monitoring, and drift management.
- Hands-on experience designing cloud-native architectures and optimizing cloud cost/performance.
- Strong stakeholder management—ability to translate business problems into technical roadmaps and measurable outcomes.
- Experience implementing CI/CD, observability, and security practices for data and ML pipelines.
- Demonstrated ability to recruit, mentor, and develop senior engineering managers and technical leaders.
- Experience in a consulting or services environment delivering enterprise AI solutions.
- Prior ownership of platform productization (APIs, SDKs, developer experience) for internal or external customers.
- Advanced degree in Computer Science, Machine Learning, or related field (or equivalent industry experience).
- Hybrid work model with flexible in-office days and a high-autonomy engineering culture.
- Competitive compensation, performance-linked incentives, and opportunities for equity participation.
- High-impact role: lead strategic initiatives, influence product direction, and mentor emerging leaders in a growth-focused environment.
We welcome senior engineering leaders who are passionate about turning ML research into reliable, production-quality systems. Apply to help shape the next generation of enterprise Data & AI platforms.
Skills: cloud,enterprise,data engineering,llm,gen ai,python,rag,ml,data,artificial intelligence,platforms,data science,mlops,agentic ai,genai
More Info
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Key Skills
drift management
model training
data platforms
CI/CD
cloud-native architectures
observability
