India: A Cornerstone of Growth for MongoDB Technical Services

Ankit Kakkar
November 29, 2023
#Culture

India has emerged as a cornerstone in our MongoDB Technical Services growth story, marked by the team’s 100% growth in just two years. Bengaluru has been at the forefront of our expansion, witnessing an incredible increase in personnel and the addition of new teams and functions to support our developer data platform. This highlights Bengaluru’s emerging role in providing critical technical assistance to our customers and partners.

Gurugram has also played a crucial role in the growth of Technical Services in India. This growth underscores Gurugram’s increasing significance as a thriving hub for MongoDB Technical Services. MongoDB’s continuing investment in expanding Technical Services in India reflects the substantial impact the team has had on MongoDB's customer success.

A look into Technical Services

We have multiple customer-facing Technical Services teams in India, each with unique roles and responsibilities. From specific product support to support for MongoDB services and Atlas cluster deployments, each team seeks individuals with strong critical thinking skills who can quickly detect, resolve, or escalate complex issues that may span various aspects of MongoDB's products and services. Our Technical Services teams are committed to delivering exceptional support to our customers through each team’s unique focus.

Building together across teams and departments

As part of their role, Technical Services Engineers (TSEs) need to partner with other supporting functions within MongoDB to ensure seamless operations and exceptional customer support.

TSEs proactively identify issues that may require escalation and involve Escalation Managers accordingly. They also identify opportunities for improvement within the MongoDB product ecosystem and pass on any feedback, feature requests, and customer insights to our Product Management and Customer Success teams.

The Technical Services team is highly collaborative and works together to solve customer problems. While each sub-team within Technical Services focuses on specific areas of expertise, there are numerous intersections that require cross-team collaboration. This collaborative approach enables team members to learn and build new skills by exploring different areas of interest.

Excellence centers

We also take pride in the technical excellence centers that we have built between our teams in India. These centers are part of our global Technical Experts ecosystem. Technical Experts help us train the Technical Services team, liaise with the development teams, and highlight pain points to the Product Management team, amongst other responsibilities.

Learning and development

We believe that an engineer's journey goes beyond a career and is about gaining knowledge, skills, and experiences. MongoDB is an integral part of this journey, offering a platform for continuous growth and development.

New hires undergo comprehensive onboarding and training to gain a holistic understanding of the MongoDB ecosystem. Engineers are encouraged to participate in cross-team rotations and have access to various learning platforms, including O’Reilly Learning and internal Product Readiness training. We collaborate closely with our Technical Services Knowledge and Training teams to deliver training sessions, fostering both technical and presentation skills and an observational learning culture.

Engineers are also provided with “protected time” to focus on their individual learning plan, allowing them to focus on delivering projects, prepping for certifications, growing their expertise in a specific area, or working in collaborative cross-skill focus groups.

Periodic hackathons offer a platform for innovation, encourage collaboration, and contribute to team-wide problem-solving efforts. In addition, MongoDB User Groups and .local events allow the team to showcase and share their knowledge externally.

By creating a learning environment where engineers can grow and achieve their goals, both our team members and the business thrive.

Enhancing support across time zones

An exciting development in our Technical Services journey is the introduction of the Swing Shift in India. This strategic initiative leverages India's engineering talent to enhance support during India and EMEA hours, further augmenting support for the surrounding regions and improving service continuity for APAC and EMEA customers.

We continue to hire in both our Bengaluru and Gurugram offices. If you’re someone who is technically talented, enjoys problem-solving, loves working with customers, and values your personal and professional growth, I encourage you to explore open roles on our careers site.

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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. 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One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. 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