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As an Engineering Manager, AI & ML (Data Collection), you will play a critical role in building and scaling the companys Unified AI/ML Data Collection Platform , enabling standardized, reliable, and scalable machine learning capabilities across the organization. This role will focus on transforming existing AI/ML and LLM-driven data systems into a cohesive platform that supports data pipelines, model lifecycle management, evaluation frameworks, and production deployment. This position requires deep technical expertise in machine learning systems, ML platform architecture, and MLOps, along with a strong ability to lead and mentor engineering teams. You will work closely with individual contributors and cross-functional partners to ensure that ML platform capabilities align with broader business objectives and AI/ML strategies. You will be deeply involved in the design, development, and operationalization of platform components, including data ingestion, feature management, model training and evaluation, and scalable inference systems. You will provide strong technical leadership, solve complex system-level challenges, and ensure delivery of high-quality, reliable, and scalable ML solutions. Your leadership will ensure that AI/ML systems are production-ready, observable, and maintainable, with a strong emphasis on performance, cost-efficiency, and governance. You will leverage your expertise in areas such as large language models (LLMs), retrieval-augmented generation (RAG), ML Operations (MLOps), distributed systems, and cloud-native architectures. You will oversee the end-to-end lifecycle of ML systemsfrom development and experimentation to deployment and monitoringwhile ensuring alignment with global engineering standards and business priorities. You will be responsible for mentoring engineers, driving technical excellence, and fostering a culture of collaboration and innovation. Your ability to partner across teams, influence technical direction, and build high-performing teams will be critical to success in this role. You will lead a multidisciplinary team of ML engineers responsible for building and maintaining the Unified AI/ML Data Collection Platform. The team focuses on developing scalable systems that support data pipelines, model lifecycle management, LLM-based workflows, and evaluation frameworks, enabling downstream teams to build and deploy AI-driven data collection solutions. Primary Job Responsibilities: AI-Powered Data Collection Systems : Lead the design and development of scalable AI-driven data collection and enrichment workflows across structured and unstructured data sources. LLM & Generative AI Workflows: Drive the implementation of LLM-based capabilities including extraction pipelines, RAG systems, prompt orchestration, summarization, classification, and automated validation workflows. Data Pipeline Engineering: Oversee high-scale ingestion, transformation, and orchestration systems that support real-time and batch data collection processes. Data Quality & Evaluation: Establish frameworks for evaluating extraction quality, model performance, hallucination risks, consistency, and overall data reliability. Technical Leadership: Provide hands-on leadership in architecture, system design, operational scalability, and engineering best practices for AI-enabled data systems. Team Leadership & Mentorship: Build and grow a high-performing engineering team while fostering a culture of ownership, collaboration, and continuous improvement. Cross-functional Collaboration: Partner closely with product management, data engineering, research, and business stakeholders to align technical investments with organizational goals. Platform Reliability & Governance: Ensure systems meet standards for scalability, observability, security, compliance, and auditability. Innovation & Continuous Improvement: Evaluate emerging AI/ML technologies, frameworks, and tooling to improve automation capabilities and developer productivity. Operational Excellence: Drive engineering best practices, Agile delivery processes, and operational maturity across the team. Hiring & Talent Development: Support recruiting, onboarding, and retention efforts while cultivating an inclusive and high-performing team environment. Company Leadership: Model company values and contribute to a culture of innovation, accountability, and collaboration. Skills and Qualifications: Bachelors, Masters, or PhD in Computer Science, Engineering, Data Science, or related field. 8+ years of experience in software engineering, with a focus on machine learning systems, ML platforms, or distributed systems. 3+ years of experience managing engineering teams and leading technical initiatives. Strong experience building production-grade ML systems , including model deployment and lifecycle management. Hands-on experience with MLOps tools and practices .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.