Experience with Infosys Limited, Cognizant, Wells Fargo US, major financial organizations in the Netherlands and Databricks—now focused on helping founders launch and grow AI-focused products.
Discuss a similar project ↗Helping founders shape AI products, launch toward PMF and build distribution. Responsibilities span customer discovery, solution positioning, founder-led sales, marketing systems and AI-agent automation for research, qualification, follow-up and reporting.
Contributed as a smaller specialist team developed into a larger organization. Combined Databricks-centred Data & AI delivery with customer discovery, technical value articulation, stakeholder workshops, solution adoption and sales enablement.
Delivered multi-cloud reliability, automation, security and data workflows across Azure, AWS and GCP. Commercial responsibilities included requirements discovery, consultative solution communication, stakeholder alignment and supporting adoption across complex accounts.
Built the enterprise foundation across Java, Spring, observability, CI/CD, Hadoop migration, database performance and production support. Supported business discovery, technical demonstrations, proposal inputs and ongoing stakeholder communication.
Results are described qualitatively to respect client confidentiality and avoid publishing unapproved performance claims.
2024–present / AI product and growth systems
Founders need to move from a promising AI idea to a focused product, credible customer evidence and a repeatable route to market without creating disconnected strategy, sales and technology workstreams.
Shape the initial product wedge, structure discovery and PMF experiments, map distribution channels and design AI-agent workflows for research, qualification, follow-up, content operations and reporting with appropriate human review.
A clearer launch path connecting the customer problem, AI implementation, founder-led sales, marketing execution and measurable learning toward product-market fit.
Technologies: AI agents · Workflow automation · CRM and marketing integrations · Data feedback loops
2019–2024 / Ex-Databricks
Customers and teams needed to turn large-scale data and AI capabilities into understandable business value while the specialist organization expanded from a smaller team into a broader operation.
Combined Databricks-centred technical depth with customer discovery, solution positioning, stakeholder workshops, adoption support and technical value articulation across data engineering, analytics, AI and cloud use cases.
Stronger alignment between business priorities, Data & AI architecture and adoption—supporting both customer outcomes and the commercial growth of a scaling specialist organization.
Technologies: Databricks · Python · SQL · Data engineering · Cloud platforms · Analytics
2018–2019 / Financial organizations, Netherlands
Complex financial environments spanning Azure, AWS and GCP required secure transformation, reliable delivery and clear communication between technical teams, decision-makers and business stakeholders.
Delivered infrastructure automation, Kubernetes, secure CI/CD, observability and SRE practices while supporting requirements discovery, solution walkthroughs, stakeholder alignment and value-focused communication.
More consistent cloud operations and delivery, with technical investments connected more clearly to reliability, governance, adoption and organizational priorities.
Technologies: Azure · AWS · GCP · Terraform · Ansible · Kubernetes · Dynatrace · Splunk
2011–2018 / Infosys, Cognizant and Wells Fargo US
Large enterprise applications and data workloads required dependable engineering, production visibility, secure delivery and modernization across established systems.
Worked across Java and Spring platforms, CI/CD, production support, observability, database performance and Hadoop migration. Supported requirements discovery, technical demonstrations, solution communication and adoption with business and engineering stakeholders.
A durable foundation in platform engineering, data systems and customer-facing technical responsibility that later expanded into cloud, Data & AI and growth-oriented work.
Technologies: Java · Spring · Hadoop · HDFS · MapReduce · AppDynamics · Splunk · Jenkins