I build AI systems that work in the real world.
From multimodal deep learning and agentic AI to production ML infrastructure — I design and ship systems that solve hard problems. I also lead the teams that build them.
8+ years in ML & AI · 15+ person cross-functional team ·
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I've spent the better part of a decade building ML systems that actually ship — from fraud detection pipelines and knowledge graph-based entity resolution in my early years, to leading teams building agentic AI platforms and multimodal deep learning models.
What I find most interesting is the space between research and production — where a promising model has to survive real data, real scale, and real users. That tension is where most of the hard decisions live, and it's where I've spent most of my career.
I lead a cross-functional team of 15+ and still stay close to the technical work — running model design reviews, setting architecture standards, and pushing for rigor in how we evaluate and iterate. I'm a strong believer that good mentorship and good engineering culture aren't separate from shipping fast; they're what make it sustainable.
Outside of work, I'm usually outdoors — hiking, training for the next expedition, or picking up something new just to stay curious and keep challenging myself.
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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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