This 10 minute interview helps Round1 gauge your readiness for DevOps roles across 100+ tech & product companies. Take this interview once, and Round1 will actively search for DevOps roles for you, find the ones where you’re likely to get shortlisted, and help share your profile at the top with recruiters. If a company expresses interest in you, you will be notified via WhatsApp or email. Role Overview: As a DevOps Engineer, you will play a critical role in managing infrastructure, automating workflows, and ensuring seamless deployment processes across diverse technology stacks. You will leverage your expertise in CI/CD, cloud platforms, and infrastructure-as-code to drive operational excellence and system reliability. This position offers the opportunity to collaborate closely with developers and SREs, contributing to scalable and automated solutions for top tech and product companies. What Topics will be covered in the Interview: Your experience with CI/CD pipelines Tool choices (Docker, Kubernetes, Terraform, etc.) Incident handling or system reliability Collaboration with developers/SREs Thought process on scalability and automation Qualifications: Required Experience: 3–10 years (flexible) Job Location: Hyderabad (Remote/On-site/Hybrid based on company policy) What we offer: Competitive compensation and benefits package Professional growth and career development opportunities Collaborative and innovative work environment Show more Show less
Role Overview Looking for a Data Engineer to design, build, and scale data infrastructure that supports core recommendation systems and personalization engines. As an early team member, you will help establish the data foundation for onboarding flows, engagement metrics, dashboards, and ML model pipelines. You’ll work closely with machine learning and product teams to build reliable data adapters and experimentation frameworks. Key Responsibilities Build and scale ETL pipelines to transform raw app events into ML-ready features Develop real-time and batch data pipelines to process user interactions and system events Contribute to feature stores, embedding pipelines, data validation tools, and freshness monitoring systems Design robust data layers to support A/B testing, metrics tracking, and model training workflows Partner with ML engineers to deliver input adapters, training loops, and embedding generation modules Continuously evolve the core data model and event schema with an emphasis on privacy, observability, and performance What We Look For Skills: 3–6 years of experience building scalable data infrastructure in high-velocity environments Exposure to recommendation systems, ML workflows, or content feeds in B2C platforms (social, media, gaming, etc.) A strong understanding of how data features impact machine learning models and outcomes Hands-on experience with modern data tooling such as Kafka, Spark, Flink, Airflow, dbt, Redis, BigQuery, or Terraform Product-first thinking with an understanding of how data systems directly affect user experience Qualifications Required Experience: 3–6 years working with scalable data systems Bonus Points: Experience in personalization or ranking models, recommender pipelines, A/B testing frameworks, or ML experimentation tools; passion for building consumer-facing products Location: Gurugram What We Offer Competitive compensation and benefits Ownership of foundational data systems with direct impact on product direction Opportunities for professional growth and cross-functional collaboration A fast-paced, collaborative, and experimentation-driven work environment Show more Show less
Role Overview Seeking a hands-on, founding Machine Learning Scientist or Principal ML Engineer to lead the design and deployment of core recommendation and personalization systems. You will own the end-to-end lifecycle of machine learning solutions—from design to deployment—while laying the groundwork for scalable, real-time ranking infrastructure and setting the technical vision and culture for a new product built from scratch. Key Responsibilities Design, develop, and deploy recommendation, ranking, and personalization systems Build real-time adaptive engines that continuously learn from user interactions and contextual signals Develop sophisticated ranking algorithms to power personalized user feeds Construct embedding systems, similarity models, and graph-based scoring frameworks Tackle cold-start and sparse data challenges through innovative ML techniques Collaborate with data, product, and engineering teams to deliver seamless user experiences Deploy models using fast iteration cycles, model registries, and observability tooling Help build and shape a high-impact ML engineering team and its technical culture What We Look For Skills: Deep expertise in recommendation systems, personalization, ranking, or search 3–10 years of experience building ML solutions in consumer-facing products (social, ecommerce, gaming, media, etc.) Familiarity with collaborative filtering, deep retrieval models (e.g., two-tower), learning-to-rank, embedding techniques with ANN search, and LLM-based strategies for sparse data Proven ability to build, evaluate, and deploy end-to-end ML pipelines Strong grasp of online/offline evaluation methods, A/B testing, and aligning ML metrics with product goals Qualifications Required Experience: 3–10 years in building and deploying personalization, recommendation, search, or ranking systems Bonus Points: Experience with vector search, graph algorithms, real-time recommender systems, cold-start problem-solving, and online experimentation infrastructure Location: Gurugram What We Offer Competitive compensation and benefits A high-impact role as a founding member of the ML team Opportunities to shape product direction and influence user experience at scale A collaborative, fast-paced, and innovation-driven environment focused on solving meaningful problems Show more Show less