Nirup Kumar Reddy Pothireddy is a highly experienced technical leader with 15+ years driving the design, development, and support of distributed systems, data science, and machine learning projects. Currently a Senior Software Engineer at Uber Technologies Inc., he has previously held leadership positions at Dolby Labs and Cisco Systems, where he guided cross-functional teams to successful outcomes. His expertise spans AI & machine learning for enterprise solutions, big data engineering, generative AI, and natural language interfaces.
In this interview, Nirup discusses the evolution of data engineering, the impact of Natural Language to Data solutions on business intelligence, and how his diverse industry experience has shaped his approach to building AI systems that deliver measurable results.
What has your journey been like from your early career to your current role as a Senior Software Engineer?
My own tech career started after completing my Master’s in Computer Science, and I have been working since the past 15+ years in technical and leadership roles at Uber, Dolby, Cisco, AT&T, and American Express. I began as a Software Engineer where I worked on distributed systems and large data pipelines, going really deep on technologies like Hive, Hadoop, and ETL design.
Leadership discovered me naturally. I headed cross-functional teams at Cisco developing AI-powered analytics for IoT platforms. It was an inflection point—apart from coding, I was guiding engineers, influencing architecture, and aligning with product objectives.
Later, at Dolby and Uber, I led groundbreaking projects, such as building machine learning models for media optimization and launching Finch, a generative AI chatbot that offers financial insights. These experiences taught critical lessons about managing ambiguity, driving innovation, and leading with clarity.
My leadership philosophy centers around an active, hands-on style, setting an example, and creating a culture centered around ownership and continuous learning. Whether providing direction on Spark optimizations or mentoring product strategy, I aim to combine technical depth with strategic thought and clear communication.
How has your experience across multiple industries influenced your perspective on AI and data engineering?
My career has spanned several industries—academia, banking, telecom, and now transportation—developing a cross-industry perspective on data and AI. I began in academia as a director of research and subsequently transitioned to banking, where I was in credit systems. What I learned there was the value of data granularity and the need for real-time processing, especially for financial transactions.
At AT&T in telecommunications, I worked with large-scale data and concentrated on privacy-preserving analytics, developing a location-based ad platform and automating purchasing pipelines. Here is where I learned to innovate while working within the constraints of compliance and governance.
Now at Uber, I bring these lessons to a day-to-day level in a high-velocity setting. Whether it’s predicting bookings or developing conversational AI such as Finch, I ensure the systems are resilient but pliable.
Working in various industries has made me more strategic and empathetic. I have understood how to bind various stakeholders together, be it in a startup or a multinational firm, and always try to deliver AI solutions that are technically sound but also well aligned with business outcomes.
Your expertise includes developing Natural Language to Data solutions. How are these technologies changing how businesses interact with their data assets?
Natural Language to Data (NL2D) is transforming how companies interact with data by removing technical barriers. Previously, access to insights needed SQL capabilities or knowledge of complex schemas—skills limited to a privileged group. NL2D democratizes that access.
At Uber, I headed the development of a generative AI-based financial chatbot that can translate natural language queries into SQL and MDX. The chatbot was, in fact, embedded right within Google Sheets, thereby allowing Finance and Accounting teams to get curated insights by just asking queries such as, “What were our net revenues by city for Q2?” without any coding.
This transformation has been making its impact. Business users self-serve insights, accelerating decision-making. With contextual intelligence driven by LLMs and metadata-driven integration, the system not only comprehends syntax but business logic as well. Teams save hours of replicated data pulls, and dashboards scale dynamically to evolving questions.
The incorporation of the tool, Finch, into their daily workflows had wrought a cultural transformation that was characterized by more meaningful data interaction, accelerating collaboration, and increased confidence in decision-making. I think that NL2D will be the standard for companies to engage with information, if it is founded upon sound data models and governance that delivers trust and integrity.
What have been your most significant contributions to AI development over your career?
Over the past 15 years, my most important work in AI has been developing intelligent, scalable systems that deliver tangible value in finance, retail, and IoT. One of the most notable is my work on Natural Language to Data (NL2D) solutions that allow business users to query complex data systems using everyday language—removing technical gatekeeping on data access and making decision-making at scale more straightforward.
In the IoT domain, I headed the development of machine learning models for real-time anomaly detection and edge-based auto-remediation. It is patented (US20220086066A1) and has been instrumental in advancing wireless networks’ dynamic thresholding.
In retail, I created location-based algorithms that guide users to optimal store locations by calculating real-time variables like inventory and traffic. That work was patented (US10235687B1) and subsequently contributed to breakthroughs in distributed caching and personalization.
Along the way, my focus has been on turning AI into solid, usable solutions that live up to real business needs—not research prototypes. My success is building systems that scale, enable users, and make a measurable impact.
How is generative AI transforming financial operations in enterprise environments?
Generative AI is fundamentally transforming financial operations by turning complex, technical workflows into intuitive, conversational experiences. Traditionally, accessing financial insights required specialized tools and knowledge of SQL or financial systems. With solutions like AI-driven financial chatbots, business users can now query vast enterprise data environments using natural language—making insights accessible, fast, and context-aware.
This shift is not just about convenience; it’s about speed, accuracy, and empowerment. Finance teams can generate reports, validate metrics, and perform ad-hoc analysis without waiting on data engineering cycles. It reduces bottlenecks, increases agility in planning and forecasting, and enables more informed, real-time decision-making.
From my experience leading such initiatives, the true impact comes when generative AI is paired with strong data governance and enterprise integration—ensuring that outputs are not just fast, but also trustworthy and actionable. In essence, it’s redefining the role of finance from being data consumers to strategic, self-serve decision-makers.
What recent advancements in scalable ETL development do you find most promising for business applications?
One of the most promising advancements in scalable ETL development is the shift toward real-time and declarative data pipelines. Tools that support streaming ETL—using frameworks like Spark Structured Streaming or Apache Flink—are enabling businesses to move from batch processing to near-instant insights, which is critical for time-sensitive operations like forecasting, fraud detection, or dynamic pricing.
Another key trend is the rise of metadata-driven and low-code ETL platforms, which improve maintainability and reduce development overhead. These systems allow for greater automation in lineage tracking, schema evolution, and pipeline orchestration—making large-scale data environments easier to manage and audit.
In my experience, combining these with modern data lakehouses and cloud-native orchestration (e.g., using tools like Airflow, dbt, or Dataform) offers both scalability and flexibility. It empowers teams to iterate quickly while maintaining trust and performance at scale.
Overall, the evolution of ETL is moving toward agility, observability, and self-serve capabilities—enabling data to be not just bigger, but smarter and more business-aligned.
As someone with patents in AI/ML, what advice would you give to engineers looking to transition from traditional software roles into specialized AI development?
My advice to engineers moving from traditional software roles into AI/ML is to start by building a strong foundation in data literacy and mathematical intuition. Unlike traditional development, AI requires understanding not just how systems behave, but why—through the lens of statistics, probability, and model behavior.
Next, focus on real-world problem framing. AI isn’t about picking the most advanced model—it’s about defining the right problem, identifying the right data, and deploying solutions that are measurable and scalable. Start with simple use cases and optimize for learning, not just outcomes.
Also, treat data engineering as a first-class skill. Clean, well-structured data pipelines often make a bigger impact than complex models. My own work in areas like IoT anomaly detection and natural language interfaces taught me that model quality is often constrained—or enabled—by upstream data architecture.
Finally, surround yourself with interdisciplinary thinking. The best AI solutions come from understanding domain context—whether it’s finance, retail, or infrastructure. And don’t shy away from contributing to patents or publications—it sharpens your innovation mindset and helps you think beyond just implementation.
AI is not a silo—it’s an extension of good engineering thinking, applied with precision and empathy.
How do you approach anomaly detection and predictive modeling when working with vast datasets across different industry contexts?
When I tackle anomaly detection and predictive modeling in large, dirty datasets, I follow a methodical but flexible process that adapts to industry-defined needs. Every industry—finance, healthcare, retail—defines anomalies differently. Fraud in finance; equipment failure in manufacturing, for example. So I begin by collaborating with domain experts to identify what “normal” and “abnormal” are and link those definitions to business KPIs.
Then, data preprocessing and profiling are necessary—handling missing values, outliers, and schema drift across structured and unstructured data. I typically leverage scalable tools like Spark, Snowflake, or Dask to handle volume and velocity.
Feature engineering is highly contextual: time features for fraud, rolling aggregates for customer behavior, or signal features in IoT. Feature stores help to ensure consistency.
Based on the scenario, I employ statistical methods, unsupervised methods like Isolation Forest or Autoencoders, or time-series prediction with LSTM or Prophet. For prediction, I leverage a mix of classical ML like XGBoost and deep learning for more complex patterns.
Post-deployment, model monitoring and retraining loops are essential so that the performance is maintained. I have real-time readiness and scale deployments via tools like SageMaker, MLflow, or Vertex AI. It is all about coupling accuracy with readiness for operations.
Which emerging applications of generative AI will have the most significant impact on enterprise automation?
Generative AI is redefining enterprise automation by targeting high-impact areas of knowledge work, streamlining operations, and enhancing decision-making. The most exciting applications are those that directly reduce manual overhead and empower users across functions.
One major breakthrough is AI agents for business processes—automating workflows like employee onboarding or invoice processing by integrating with systems like HR, IT, and compliance. These agents cut costs and drastically reduce turnaround times.
Natural language interfaces are another game changer. By layering generative AI over tools like Salesforce or ServiceNow, non-technical users can run reports or trigger workflows with simple prompts—like asking, “Show all deals over $100K closing this quarter.”
We’re also seeing AI transform document intelligence. It can now extract and reason over massive collections of contracts, policies, and reports—automating audits, compliance checks, and risk assessments.
Beyond that, AI-driven code generation, synthetic data creation for training, enterprise content automation, and decision support tools are rapidly gaining traction. Each of these applications addresses a pain point—whether it’s improving developer velocity, protecting sensitive data, or delivering real-time business forecasts.
Together, these innovations signal a shift toward intelligent, self-serve enterprise systems that scale human decision-making and productivity.
Could you share an example of a particularly challenging data engineering problem you’ve solved?
One of the most challenging data engineering problems I tackled involved building a real-time analytics pipeline for a global e-commerce company. The goal was to unify clickstream data, transaction logs, and support interactions from disparate sources like Kafka, S3, and REST APIs—all while maintaining sub-5-second latency.
The major hurdles were schema drift, late-arriving events, and inconsistent data quality. Kafka topics often had evolving Avro schemas, which would break downstream processes. Some data arrived up to 30 minutes late due to offline caching, and we were dealing with malformed JSON, incorrect timestamps, and missing IDs.
To solve this, we implemented schema versioning via Kafka with Confluent Schema Registry and built an adaptive schema parser. Apache Flink, chosen over Spark for its fine-grained windowing, allowed us to manage late data with sliding windows and watermarking. We also introduced a feature store using Redis and Hudi for fast aggregation and reliable backfills.
Validation was enforced using Great Expectations, and we monitored everything through custom Grafana dashboards. The result? We reduced latency from 20 seconds to under 5, handled over a million events per minute, and gave business teams trusted, real-time data for faster, smarter decisions.
How do you see the role of data engineers evolving over the next five years as AI becomes increasingly integrated into enterprise systems?
As AI becomes deeply embedded in enterprise systems, the role of data engineers is evolving rapidly. They’re no longer just backend pipeline builders—they’re becoming strategic enablers of intelligent systems. Over the next few years, I see five major shifts defining this transformation.
First, data engineers are moving from building ETL pipelines to owning data as a product—complete with SLAs, versioning, and governance. Clean, reusable data is foundational for AI success.
Second, there’s closer collaboration with AI/ML teams. Data engineers now co-own real-time feature pipelines, ensuring model accuracy, reproducibility, and integrity.
Third, data governance and ethics are front and center. With growing regulatory scrutiny, engineers are using tools like DataHub, Great Expectations, and synthetic data generators to ensure fairness, lineage tracking, and compliance.
Fourth, generative AI is automating repetitive tasks like SQL generation and documentation. This frees engineers to focus on architecture, orchestration, and high-impact design work.
Lastly, they’re becoming the architects of low-latency systems powering AI agents and real-time personalization. Skills in event-driven systems, vector databases, and graph structures are increasingly critical.
In short, data engineers are now central to building the intelligent, autonomous infrastructure driving the future of enterprise innovation.
