Passionate and innovative AI engineering leader with broad technical expertise and a keen intuition for solving real-world problems through data-driven approaches and cutting-edge technology.
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I've always been driven by a curiosity for how data and algorithms can solve real-world problems. I thrive at the intersection of data, design, and leadership—where creativity meets technical rigor. I bring a strong foundation in computer science, with the ability to quickly grasp complex problems, stay on top of the latest tech advancements, and adapt seamlessly to different tech stacks. I'm someone who genuinely loves learning—whether it's mastering a new technology at work or diving into a personal passion.
Teaching and mentoring bring me a lot of joy—especially when it comes to breaking down complex technical topics and making them accessible to a wide range of audiences, whether they’re engineers, business stakeholders, or curious learners. Outside of work, you’ll find me hiking, chasing adventures, powering through CrossFit or yoga sessions, exploring wellness, planning and training for next expedition, or picking up a new sport just for the thrill of it.
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Integrated visual, textual & spatial modalities using transformers to enhance understanding and extraction from document images.
Proficient in object detection, segmentation, classification, and document image processing, with hands-on experience using Mask R-CNN, YOLO, and image transformers.
Proficient in Attribute extraction, sentiment analysis, conflict detection, and building Customer 360 profiles using knowledge graph–based similarity and disambiguation techniques
Integrated and deployed LLMs with fine-tuning and scalable inference; built RAG pipelines for contextual extraction; designed agentic workflows for multi-step decision automation
Custom KPIs for precision, recall, F1-score, and business impact
A/B tests, model benchmarking, and controlled comparisons
Root cause investigations across edge cases and data biases
Iterative evaluation & transparent reporting with dashboards, logging tools to support stakeholder decisions
Managed Kubernetes-based autoscaling, fault-tolerant deployments, and inference workloads using MCP servers for scalable, production-grade model hosting
Designed low-latency, high-throughput ML inference systems with clean, scalable REST APIs for seamless integration of models and data pipelines
Designed robust continuous integration and delivery pipelines for AI workflows using Jenkins, with integrated monitoring and logging via Grafana, Prometheus, Loki, and centralized log management tools
Strong foundational expertise in development, configuration, and deployment on the AWS platform, enabling adaptability and quick ramp-up across any cloud environment.
Applied iterative, problem-solving frameworks to translate abstract business needs into actionable product features and data-driven workflows.
Integrated scalable training and inference pipelines with modular components—enabling extensibility for new data domains and customer requirements.
Leveraged quantitative research and competitive data to identify customer-centric challenges and inform product roadmap decisions.
Designed UI components as “cognitive bricks” based on application interaction data, improving reusability and reducing redundant development.
Led a cross-functional team of 15+ professionals including data scientists, ML engineers, developers, and DevOps fostering a culture of collaboration, accountability, and high performance. Successfully orchestrated team efforts to deliver end-to-end AI solutions across complex domains.
Oversaw the full product lifecycle from ideation to production using Agile methodologies. Ensured rapid iteration, clear prioritization, and continuous integration of stakeholder feedback to accelerate time-to-value for AI products.
Led the research and development of advanced AI technologies and successfully transformed research innovations into scalable, production-ready features, accelerating both experimentation and delivery.
Scaled high-performing teams through structured hiring processes, hands-on technical mentorship, and personalized training sessions. Provided 1:1 mentorship and career development coaching, helping team members grow into leadership and staff roles.
Defined and executed technical roadmaps aligned with organizational goals and market needs. Balanced short-term deliverables with long-term scalability by making architecture, infrastructure, and talent investment decisions.
Championed the integration of AI solutions into real-world use cases in finance, insurance, and banking. Worked closely with business stakeholders to translate domain problems into technical objectives, ensuring measurable ROI and adoption.
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Led a cross-functional team of 15+ professionals—including data scientists, ML engineers, developers, and DevOps—to successfully develop and launch the Collatio SaaS product using Agile methodologies. Oversaw the entire product lifecycle from ideation to deployment, driving feature development and implementing advanced algorithms for document parsing and reconciliation.
Introduced Kubernetes-based infrastructure to enable scalable and efficient deployment of LLM models, ensuring high availability and optimized resource utilization in production.
Directed R&D efforts using Multimodal LLM, agentic workflow for critical product features, ensuring timely delivery and adherence to project timelines.
Led recruitment, mentorship, and development of cross-functional teams, aligning individual goals with project objectives to optimize AI products in Financial Services, Insurance, and Banking sectors.
Managed a team of 10+ data scientists and engineers in the research, design, and development of Collatio, an intelligent document processing solution, deploying multimodal deep learning models for OCR, document classification, attribute extraction, and object detection on unstructured documents.
Designed and developed distributed enterprise data products with modular architecture and scalable design. Hands-on expertise in model training, deployment, experimental design, and hypothesis testing.
Developed a robust feature store tailored for the banking industry, designed to seamlessly integrate with Anti-Money Laundering (AML), fraud detection, and customer segmentation pipelines. This solution enabled efficient feature management, enhancing model performance and scalability across critical data-driven processes in the banking domain.
Designed and implemented an advanced Entity Resolution pipeline for customer due diligence, using machine learning and knowledge graph-based similarity engines to build Customer 360.
Designed and implemented predictive user behaviour models for security breach detection in banking applications using knowledge graphs and unsupervised anomaly detection.
Applied advanced machine learning and NLP techniques on structured and unstructured data, improving fraud detection recall by 30% and precision by 10% in ACH transactions, and developed AI-based solutions for conflict detection in investment banking.
Analyzed high-volume credit card transactions and developed a descriptive analytics model with an interactive dashboard to visualize sales trends and identify causal effects.
Taught computer science courses and developed models such as sentiment analysis, student success prediction, and dashboard to generate reports and insights.
Developed data transformation pipelines for resolving discrepancies and optimising workflows across Oracle Apps and SAP.
Worked on requirements gathering by collaborating with clients for developing tools for developing tools for supplier buyer collaboration. Designed and implemented solutions for parts review, purchase order collaboration and shipment.
Taken part in descriptive analytics on large amount of data residing on Oracle and SAP warehouses; figure out discrepancies and anomalies in data to provide key insights to clients. Taken part in implementation, testing and migrating solutions for enhancement requests submitted by users.
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