Hello, I’m Archana Chippalthurthi. I work as a Manager and MLOps Engineer in Capgemini’s Insights and Data business unit. My expertise spans Python, machine learning, MLOps, and cloud technologies. Since joining Capgemini in 2020, I’ve worked across Azure, AWS, and Google Cloud Platform, while also contributing to delivery, stakeholder engagement, team leadership, and mentoring.

How does a usual day working at Capgemini look like?

    A typical day at Capgemini is a good mix of technical delivery, collaboration, problem-solving, and mentoring. It often starts with a daily stand-up, where we review ongoing tasks, priorities, dependencies, and timelines. I work on building and managing end-to-end machine learning and MLOps pipelines, from understanding business requirements and preparing data to tracking models and deploying them through CI/CD. I also use tools across AWS, Azure, and GCP, while collaborating with stakeholders, guiding team members, and contributing to strategic initiatives.

    Talk about a time you’ve felt supported in your development at Capgemini.

    I’ve felt supported at Capgemini whenever I’ve had the chance to learn something new. I started with Azure experience, and when I joined an AWS project, I built my knowledge through internal learning and certification support. I then applied that learning in a real client project, which helped me gain confidence. Later, I also got the opportunity to work on Google Cloud Platform, strengthening my skills across AI, MLOps, and cloud technologies.

    What is your experience of inclusion at work?

    My experience of inclusion at Capgemini has been truly positive. I feel my ideas are heard and my contributions are valued by managers and senior leaders. As a woman professional and mother, the flexibility and support have helped me balance work, family, and well-being while continuing to grow, learn, lead, and build a meaningful career after my career break.

    Talk about a time when you were able to be innovative.

    Capgemini has given me many opportunities to explore AI, generative AI, agentic MLOps, cloud technologies, and architecture. One experience that stands out was an internal generative AI hackathon in 2025, where we have built a proof of concept for an AI-powered product recommendation solution. The idea used product attributes and visuals to make recommendations more relevant. Winning first prize was a proud moment, and it showed me how innovation grows when we are encouraged to experiment, learn, and collaborate.

    Can you share a story of how your work at Capgemini made a real impact with a client?

    In one client engagement, I managed several responsibilities, from project delivery and product ownership to stakeholder communication, team leadership, Scrum Master duties, and end-to-end MLOps delivery. I helped develop scalable AI and machine learning solutions aligned with the client’s business goals, while keeping the focus on value and measurable outcomes. I also worked on an NLP proof of concept for document understanding and extracting invoice details and dates, which we completed in three months and received positive appreciation. These experiences showed me that creating a real client impact requires more than technical expertise. It also requires ownership, collaboration, and a strong understanding of the client’s business needs.

    What piece of advice would you give to a new joiner starting in your role?

    Build strong technical foundations in machine learning, MLOps, CI/CD, deployment, monitoring, and cloud platforms, but also focus on communication, ownership, and collaboration. Stay open to different projects, keep learning through certifications and mentorship, and trust your abilities. With resilience, curiosity, and the right support, you can build a rewarding career in AI, MLOps, and cloud technologies.