Manager Project in India

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. What You’ll Be Doing Design, build, and operate highly scalable, reliable, and secure infrastructure powering our production systems across AWS and GCP. Lead major reliability and modernization initiatives, including container platform migrations (e.g., ECS to EKS/GKE) and microservice enablement across multi-cloud environments. Serve as a technical authority in Kubernetes (EKS and GKE), cloud infrastructure (AWS and GCP), and modern CI/CD practices (GitOps, automation pipelines). Partner with development teams to architect and enable microservice-based applications, ensuring production readiness, scalability, and observability. Implement and manage infrastructure as code (Terraform, Ansible) to automate provisioning, scaling, and configuration management across multiple cloud providers. Drive improvements in observability, performance, and cost efficiency through robust monitoring, logging, and alerting systems that span AWS and GCP. Champion SRE best practices — defining SLOs/SLIs, conducting blameless postmortems, and continuously improving incident response. Lead complex technical projects from

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. What You’ll Be Doing Design, build, and operate highly scalable, reliable, and secure infrastructure powering our production systems across AWS and GCP. Lead major reliability and modernization initiatives, including container platform migrations (e.g., ECS to EKS/GKE) and microservice enablement across multi-cloud environments. Serve as a technical authority in Kubernetes (EKS and GKE), cloud infrastructure (AWS and GCP), and modern CI/CD practices (GitOps, automation pipelines). Partner with development teams to architect and enable microservice-based applications, ensuring production readiness, scalability, and observability. Implement and manage infrastructure as code (Terraform, Ansible) to automate provisioning, scaling, and configuration management across multiple cloud providers. Drive improvements in observability, performance, and cost efficiency through robust monitoring, logging, and alerting systems that span AWS and GCP. Champion SRE best practices — defining SLOs/SLIs, conducting blameless postmortems, and continuously improving incident response. Lead complex technical projects from conception to completion, managing timelines, and technical dependencies across teams. Mentor engineers across teams, fostering a culture of reliability, automation, and continuous learning. Collaborate with security and compliance partners to ensure infrastructure adheres to best practices and standards (e.g., IAM Federation, Workload Identity). Participate in t

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. What You’ll Be Doing Design, build, and operate highly scalable, reliable, and secure infrastructure powering our production systems across AWS and GCP. Lead major reliability and modernization initiatives, including container platform migrations (e.g., ECS to EKS/GKE) and microservice enablement across multi-cloud environments. Serve as a technical authority in Kubernetes (EKS and GKE), cloud infrastructure (AWS and GCP), and modern CI/CD practices (GitOps, automation pipelines). Partner with development teams to architect and enable microservice-based applications, ensuring production readiness, scalability, and observability. Implement and manage infrastructure as code (Terraform, Ansible) to automate provisioning, scaling, and configuration management across multiple cloud providers. Drive improvements in observability, performance, and cost efficiency through robust monitoring, logging, and alerting systems that span AWS and GCP. Champion SRE best practices — defining SLOs/SLIs, conducting blameless postmortems, and continuously improving incident response. Lead complex technical projects from conception to completion, managing timelines, and technical dependencies across teams. Mentor engineers across teams, fostering a culture of reliability, automation, and continuous learning. Collaborate with security and compliance partners to ensure infrastructure adheres to best practices and standards (e.g., IAM Federation, Workload Identity). Participate in t

About the Team OpenAI’s mission is to build safe artificial general intelligence (AGI) which benefits all of humanity. This long-term undertaking brings the world’s best scientists, engineers, and business professionals into one lab together to accomplish this. In pursuit of this mission, our Go To Market (GTM) team is responsible for helping customers learn how to leverage and deploy our highly capable AI products across their business. The team is made of Sales, Solutions, Support, Marketing, and Partnership professionals that work together to create valuable solutions that will help bring AI to as many users as possible. About the Role Our Sales team has a unique mission to help customers understand the deep impact that highly capable AI models can bring to their business and users. This role is a mixture of technical understanding, vision, partnership, and value-driven strategy. You’ll be a key driver of opportunities through the entire sales cycle, from pipeline generation to closure. You’ll work with researchers, engineers, and solution strategists to help customers evolve their industry with AI. This role will be based in Delhi. In this role, you will: Manage a small number of key accounts, developing and executing strategies for a comprehensive account plan. Lead our enterprise prospects’ journeys from consideration to successful deployment. Partner with solutions and research engineering to build and execute complex customer programs and projects. Own a consumption revenue target. Manage consumption revenue forecasts. Analyze key account metrics to create reports and provide insights to internal and external stakeholders. Closely monitor the industry landscape (people, competitors, partners, etc.) to contribute to product roadmap and other corporate strategies. Collaborate with solutions, marketing, communications, business operations, people operations, finance, product management, and engineering. Support the recruitment and onboarding of other teammates.

Who Are We? Postman is the world’s leading API platform, used by more than 45 million+ developers and 500,000 organizations, including 98% of the Fortune 500. Postman is helping developers and professionals across the globe build the API-first world by simplifying each step of the API lifecycle and streamlining collaboration—enabling users to create better APIs, faster. The company is headquartered in San Francisco and has offices in Boston, New York, Austin, Tokyo, London, and Bangalore - where Postman was founded. Postman is privately held, with funding from Battery Ventures, BOND, Coatue, CRV, Insight Partners, and Nexus Venture Partners. Learn more at postman.com or connect with Postman on X via @getpostman. P.S: We highly recommend reading The "API-First World" graphic novel to understand the bigger picture and our vision at Postman. About the Team : The Identity and Access Management team is responsible for building a secure, reliable, and seamless identity platform for all Postman users. This team owns the systems behind authentication, session management, access control, service accounts, invites, and other foundational identity capabilities that power the product. Postman’s identity layer is a critical part of the product experience. It sits on the path of every authenticated request and enables users, teams, and systems to access Postman securely and collaborate effectively. The team works on a mix of core platform improvements, new product capabilities, and reliability/security investments. What You’ll Do: Design, build, and evolve backend systems that power Postman’s identity and authentication platform. Own projects end to end: clarify requirements, drive technical design, implement solutions, manage rollout, and support production. Work closely with Product, Design, and Engineering teams to scope, build, test, and launch new features and platform improvements. Improve system reliability, scalability, observability, and operational readiness for service

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. The Commercial Account Executive is responsible for building Commercial-level client relationships within the Bangalore market. Individuals who excel at this job have the ability to prospect, develop, and close business within a timely manner while focusing on the clients' requirements. The Commercial AE must have the confidence and ability to negotiate and close agreements with clients and support new customers through our onboarding process. This role is a unique opportunity to contribute in a meaningful way to high visibility, high impact projects at a very exciting time for the company. Snowflake is an innovative, high-growth, customer-focused company in a large and growing market. If you are an energetic, self-managed professional with experience managing a complex sales process and possess excellent presentation and listening skills, organisation and contact management capabilities, we'd love to hear from you. AS A COMMERCIAL ACCOUNT EXECUTIVE, YOU WILL: Achieve sales quotas for allocated accounts on a quarterly and annual basis by developing a sales strategy in the allocated territory with a target prospect list and a regional sales plan Develop marketing plans with the marketing team to drive revenue growth Be the trusted advisor to the customer by understanding the

About Paytm Paytm is a pioneer of digital payments in India, serving over 450 million consumers and 45 million merchants across payments, financial services, and commerce. Over the years, Paytm has built deep in-house capabilities across technology, data, and operations to operate at scale with high reliability. Paytm is building a full stack AI platform focussed on Inference and Agents, enabling large enterprises to deploy AI driven automation across sales, service, operations, and analytics. The Inference and Agentic AI team operates as a cross functional unit spanning engineering, product, data science, business management, and sales, and owns the full lifecycle of AI solutions from opportunity discovery to deployment and scale. Role Overview Paytm is looking to hire a Client Onboarding Director to own implementation delivery and client onboarding governance for Paytm’s AI Inference and Agentic AI products across enterprise clients. This is a managerial role that will lead Client Onboarding Managers and ensure that enterprise deployments move smoothly from sales closure to go live and early adoption. The role sits at the intersection of client teams, product, engineering, business, and sales, and is responsible for converting signed enterprise deals into successful, timely, and scalable deployments. The candidate will own delivery planning, integration governance, risk management, stakeholder communication, and post go live stabilization across enterprise AI agent deployments. The role requires strong program management, technical understanding, client handling, and ability to drive execution across multiple internal and external teams. Key Responsibilities Own end to end delivery governance for enterprise AI agent deployments from sales handoff to go live and stabilization. Lead the implementation planning process across scope, timelines, milestones, dependencies, risks, and success metrics. Ensure every enterprise deployment has a clear project plan, own

ABOUT THIS ROLE GHX is standing up a new Human-in-the-Loop (HiTL) operations team in Hyderabad to support its ADM (Automated Document Management) platform — an AI-powered document processing system replacing legacy UiPath automation for GFax North America. The platform processes over 2.2 million Purchase Orders per year and targets a 75–85% fully-automated (touchless) rate. The team handles every document the AI cannot process autonomously. The Operations Supervisor is building this team from zero and is accountable for everything within it: throughput, field accuracy, team development, day-to-day operations management, and real-time coordination with GHX Engineering and Product teams in the United States. This is a high-visibility, inaugural role — the team’s accuracy and throughput directly determine the speed and confidence with which GHX can expand the ADM platform beyond GFax North America. KEY RESPONSIBILITIES People & Performance Management — 70% Directly supervise 15-20 Document Review Specialists; own rostering across rotational shifts, attendance tracking, and queue capacity planning against daily document volume Manage operations SLA – TAT, Quality, along with people metrics Own day-to-day operations management: shift rosters and week-offs, leave planning, planned & unplanned shrinkage, absenteeism control, and real-time reallocation of staffing to protect SLAs & operations performance metrics Set and communicate daily, weekly, and monthly throughput, AHT, and accuracy targets; monitor individual and team performance against SLA and productivity standards Participate in and present at weekly, monthly, and quarterly business reviews (WBR / MBR / QBR) — performance against KPIs, root-cause analysis of misses, and committed corrective actions Own the full performance cycle for direct reports, including goal setting, mid-year and annual appraisals, with documented performance ratings, development plans, and calibration against peer supervisors Con

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. You Will: Data Architecture and Design: Designing and overseeing the architecture of scalable and reliable data platforms, including data pipelines, storage solutions, and processing systems Data Modelling and Management:Developing and implementing data models, ensuring data quality, and establishing data governance policies Data Pipeline Development: Building and optimising data pipelines for ingesting, processing, and transforming large datasets from various sources Performance Optimisation: Identifying and resolving performance bottlenecks in data pipelines and systems, ensuring efficient data retrieval and processing Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform Data Security and Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data Perform other duties as assigned You Have: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 10+ years of experience in data engineering or a similar role. Enterprise SaaS software solutions with high availability and scalability Solution handling large scale structured and unstructured data from varied data sources Experience in building and maintaining data platform systems such as distributed compute,

Fiduciary Trust International is a premier investment and wealth management firm with a commitment to growing and protecting wealth across generations. We offer a dynamic and collaborative approach to managing wealth for high-net-worth and ultra high-net-worth individuals and families, family offices, endowments, foundations, and institutions. Our investment managers, tax and estate planning professionals work together to develop holistic strategies to optimize clients’ portfolios while mitigating the impact of taxes on their wealth. As a fiduciary, the guidance we provide is in the best interests of our clients, without conflict or competing benefits. We offer boutique customization and deep expertise in specialized investment, tax and planning strategies alongside sophisticated technology and custody platforms. Fiduciary Trust International is owned by Franklin Templeton, a dynamic firm that spans asset management, wealth management, and fintech, giving us many ways to help investors make progress toward their goals. With clients in over 150 countries and offices on six continents, you’ll get exposed to different cultures, people, and business development happening around the world. About the Department: Our AI Engineering team develops and scales production-ready Generative AI and Agentic AI solutions that help solve complex business challenges and accelerate innovation across the organization. The team brings together AI engineers, architects, and business stakeholders who work collaboratively to design, build, and deploy secure, enterprise-grade solutions on Microsoft Azure. Joining this team offers opportunities to work with emerging technologies, influence AI standards and best practices, and contribute to high-impact initiatives in a collaborative and growth-oriented environment. How Y

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Job Description/ Responsibilities: Designing, developing and maintaining stable and reliable AI/ML Ops platforms / pipelines Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time. Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production. Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow. Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data Technology Eva

About the Role We are seeking an experienced Privacy Lead to establish, manage, and continuously enhance the organization's Privacy Program across business functions, products, technology platforms, and third-party ecosystems. The ideal candidate will possess deep expertise in privacy regulations including India's Digital Personal Data Protection Act (DPDP Act), GDPR, and privacy governance frameworks. The individual will partner closely with Legal, Compliance, Information Security, Product, Engineering, Risk, HR, and Business teams to ensure privacy-by-design principles are embedded into organizational processes and technology solutions. The Privacy Lead will serve as the subject matter expert for privacy governance, regulatory compliance, privacy risk management, data subject rights management, privacy impact assessments, and data protection controls. Key Responsibilities Privacy Governance & Strategy -Develop and execute the enterprise-wide Privacy Program and Privacy Governance Framework. -Define privacy policies, standards, procedures, and controls aligned with regulatory requirements. -Establish Privacy-by-Design and Privacy-by-Default principles across products and services. -Build privacy risk management and privacy control monitoring mechanisms. -Advise executive leadership on emerging privacy risks and regulatory developments. -Act as the organization's privacy subject matter expert for audits, inspections, and regulatory reviews. Regulatory Compliance Lead compliance initiatives for: -Digital Personal Data Protection Act (DPDP Act), India -GDPR (General Data Protection Regulation) -ISO 27701 Privacy Information Management System -RBI data governance and customer data protection requirements -Applicable privacy and data protection obligations relevant to the Banking, FinTech, Payments, and Technology sectors Responsibilities include: -Regulatory gap assessments -Compliance monitoring -Privacy control implementation -Privacy risk assessmen

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and