Product Operations Managers are LinkedIn’s cross functional team communicators and collaborators shaping products/solutions using analytics to represent the voice of our customers/members and customer support & success teams to help improve LinkedIn’s product health and quality.
Responsibilities
Work with product, operations, marketing, design, and engineering teams to influence and shape the product strategy and roadmap
Aggregate and analyze member issues using internal tools and queries (i.e., SQL) of all available data sources to gather user insights to influence and shape the product roadmap
Collaborate closely with product and design teams to ensure operational team processes are factored into new product development plans and designs
Drive creation of LinkedIn help center content creation in collaboration with LinkedIn’s content writers
Maintain strong domain knowledge and organization of the product areas in your purview, across both technology and operations
Requirements
Bachelor’s degree or equivalent relevant work experience
Key technical resource for Customer Success Organisation, handling implementation through post-go-live support, with a focus on LinkedIn Hiring integrations in Asia Pacific, particularly India.
Responsibilities
Lead successful onboarding and activation of customers on LinkedIn Talent Solutions, with a focus on Hiring integrations such as job ingestion and job-posting integrations, Recruiter System Connect and Apply Connect, supporting integration, authentication and consulting efforts as needed.
Own complex post-activation technical issues and escalations: diagnose root causes, coordinate fixes with customers, ATS partners and internal Support, Product and Engineering teams, and keep customers informed until resolution.
Consult with a diverse portfolio of customers across India and Asia Pacific, from large, complex enterprises to mid-sized organisations, and support APAC queues alongside peers in other regions.
Build trusted, strategic relationships with senior talent acquisition, HR and HR technology leaders at customer organisations.
Conduct technical discovery sessions to translate business objectives into documented functional, integration, security, data and operational requirements.
Requirements
BA/BS degree in Computer Science, Computer Engineering or a related technical field, or equivalent practical experience
Nice to have: Applicant tracking systems (ATS) integration
Define what reliability means for a GPU-accelerated AI platform and make it measurable. You will own the service-level indicators and objectives for the K0rdent Observability Framework (KOF) — deriving meaningful SLIs from the signals the platform already emits, and exposing them to Platform Administrators through a clean API. Work spans hybrid, edge, and air-gapped deployments built on the Mirantis K0rdent stack.
Responsibilities
Define SLIs and SLOs based on the signals available across the platform — Kubernetes, bare-metal hosts, and NVIDIA infrastructure (BMC, InfiniBand, NVLink, UFM).
Design and build the API that exposes SLIs and reliability state to Platform Administrators and downstream systems.
Establish alerting and error-budget practices that maximize signal and minimize noise.
Partner with infrastructure, storage, and networking teams to ensure the right signals are instrumented and collected.
Diagnose reliability and performance issues across the observability stack and drive their resolution.
Requirements
Nice to have: Bare-metal monitoring
Nice to have: NVIDIA infrastructure monitoring (BMC/Redfish, InfiniBand, NVLink, UFM)
Collect and prepare data for training and evaluating multimodal foundation models, develop and optimize large-scale language models like GANs and VAEs, and fine-tune Large Language models.
Responsibilities
Handle data preprocessing, augmentation, and generation of synthetic data
Design and develop backend services using Python or .NET to support OpenAI-powered solutions (or any other LLM solution)
Develop and Maintaining AI Pipelines
Work with custom datasets, utilizing techniques like chunking and embeddings, to train and fine-tune models
Integrate Azure cognitive services (or equivalent platform services) to extend functionality and improve AI solutions
Lead and build scalable data pipelines and lakehouse solutions using Databricks, Spark, and SQL, while mentoring junior engineers and ensuring data quality and governance.
Responsibilities
Design, build, and maintain scalable data pipelines and processing solutions using Databricks, Apache Spark, PySpark, Python, and SQL.
Develop robust ingestion, transformation, and publishing processes for batch and near-real-time workloads.
Engineer reusable frameworks and patterns to improve development consistency and reliability.
Optimize Databricks workloads for efficiency, reliability, and cost.
Support performance tuning, workload optimization, monitoring, and production support for Databricks-based solutions.
To develop and maintain collection scorecards, segments for portfolio intervention, and transaction-level authorization strategies and oversee implementation of credit & fraud decision engine deployment for the organization.
Responsibilities
Develop customized collection scorecards for each assigned delinquency stage
Develop self-cure models for customized collection interventions & cost control
Develop micro-segments for effective portfolio interventions & to maintain acquisition quality
Develop transaction-level scorecards as input to the authorization strategy and cross-sell qualification
Bridge emerging technologies with maritime applications and customer needs, leading technical projects and developing assurance frameworks for advanced software-intensive and AI-enabled systems.
Compensation
Competitive salary along with profit share
Responsibilities
Partner with Korean shipyards, R&D teams, and maritime customers to understand and support their digitalisation and digital transformation (DX) initiatives.
Identify opportunities by translating customer requirements and emerging technologies into technical proposals and projects.
Lead and deliver technical projects in collaboration with customers and DNV’s international experts.
Develop assurance frameworks for advanced software-intensive and AI-enabled systems.
Evaluate simulation-based verification activities, including MIL, SIL, and HIL testing, focusing on model validity, test coverage, traceability, and uncertainty management.
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