Lead Software Engineer Forward Deployed Engineer Full-Stack & AI Engineer
I turn real-world business problems into working software, end to end. For 10+ years I've combined architecture, frontend and backend development, cloud and integrations, and now applied AI, delivering enterprise platforms for financial services, telecom, automotive and manufacturing.
I don't work inside a single framework. These six areas fit together, and each links to the details below.
Angular and React frontends, Java/Spring Boot, Node.js and Python services, built as one system.
ML inference shipped in production, plus active learning in LLM APIs, RAG and AI agents.
From a real business problem to POC, integration and a deployed, iterating solution.
Micro-frontends, design systems, governance across product squads, and mentoring.
AWS, containers and CI/CD, API contracts, OAuth2 flows, Kafka and real-time streams.
Enterprise platforms I've delivered, and the experiments I'm building as I learn.
“I am a full-stack engineer who combines software architecture, frontend and backend development, cloud technologies, and applied AI to solve real-world business problems. I believe strong engineering fundamentals and modern AI tools enable developers to learn unfamiliar technologies quickly, adapt across ecosystems, and deliver end-to-end solutions rather than remain limited to a single framework.”
I'm a Lead Software Engineer with 10+ years of experience architecting and delivering enterprise-scale applications, much of it as client-facing delivery for banking, investment, telecom, automotive and manufacturing organizations. I take work from the business problem through design, build, integration and deployment.
On the frontend I build with Angular (AngularJS through Angular 18) and React, with TypeScript, RxJS and NgRx. On the backend I work in Java and Spring Boot, Node.js and Express, and Python, with REST and GraphQL APIs, Kafka, AWS and CI/CD. I focus on performance, security, design systems and scalable, distributed architecture.
As a technical leader I run architecture governance, code and design reviews, POCs and framework evaluations, and I mentor engineers. I've shipped ML inference in production and I'm actively building expertise in LLMs, RAG, AI agents and AI-augmented engineering.
Frontend, backend, data, cloud, architecture, AI and delivery. Solid badges are professional, production experience from my resume. Dashed badges are what I'm currently learning or exploring.
I've shipped ML inference in a production platform. LLM application engineering (APIs, RAG, agents and tool calling) is my active learning focus, and each card below is labelled accordingly.
Defined integration patterns that bridge TensorFlow.js APIs with Angular's DI system through custom InjectionTokens and factory providers, making ML inference services tree-shakeable and testable for AI-powered personalization.
Learning to build with LLM APIs from ChatGPT, Claude and Gemini: prompt engineering and tool calling, and how to stream and govern AI features in enterprise UIs.
Learning retrieval-augmented generation end to end: chunking and embedding content, vector databases, and grounding LLM answers in an organization's own data.
Exploring agents that plan, call tools and APIs, and automate multi-step workflows, with my integration background (auth, contracts, retries) applied to making them reliable.
Using GitHub Copilot, Claude and ChatGPT to speed up understanding, prototyping and debugging, alongside the guardrails I already enforce: strict TypeScript, type-check gates and automated tests in CI.
[Describe a retrieval-augmented generation, agent or LLM integration you've built: the problem, your architecture, and the outcome.]
The assistants, coding agents, research tools and open-model platforms I use and explore to learn faster, prototype and debug.
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Much of my career has been client delivery: enterprise engagements through Accenture and Pattem Digital for banking, telecom and manufacturing. Here's how I take a problem from first conversation to working software, with examples from that work.
Work alongside business and engineering stakeholders to find the real constraint before writing code.
Turn needs into service boundaries, API contracts and an architecture the team can deliver.
Prove the approach with a focused proof of concept before committing the platform to it.
Deliver the UI, the services behind it and the tests that keep it working.
Connect securely to identity, APIs, real-time data and ML services in the customer's environment.
Ship in small, independent increments with automated quality gates, then improve from feedback.
I believe we are living in an era where learning new technologies is more accessible than ever. A strong engineering foundation, curiosity, and AI-powered tools make it possible to adapt across technology ecosystems. From Angular and React to Java, Python, cloud platforms, and emerging AI technologies, I embrace continuous learning and turn new ideas into working solutions.
Adapt quickly to new programming languages, frameworks, and tools.
Learn through hands-on experimentation and focused practice.
Use AI coding assistants to accelerate understanding, prototyping, debugging, and development.
Apply transferable engineering fundamentals across different technology stacks.
Build practical solutions instead of limiting myself to a single ecosystem.
A target I set for myself: get hands-on with an unfamiliar language, framework or tool fast enough to build something working in about a week. It's an ambition that keeps me learning, not a claim of instant mastery.
I own the architecture, set the standards and governance, and help the engineers around me improve.
Behind every UI I build there's a service layer, an identity flow, a data stream and a pipeline that ships it. I work on all of these.
EC2, S3, Lambda, CloudFront, API Gateway, ECS and EKS, with Docker and Kubernetes for containerized delivery.
Typed API contracts, REST and GraphQL, Spring Boot and Node.js services, and secure OAuth2 request pipelines with correlation and retry.
Kafka and WebSocket streams with backpressure operators and exponential-backoff reconnection, pushing live data to dashboards without polling.
Jenkins, GitHub Actions and CircleCI pipelines with parallel lint, type-check, test, build and bundle-size regression stages.
Growing my cloud operations depth beyond pipelines and containers.
Enterprise engagements across investment management, banking, telecom, automotive manufacturing and healthcare.
Enterprise platforms I've delivered (client work under NDA, so architecture walkthroughs are available on request), followed by the engineering experiments I'm building as I learn.
Hands-on builds where I try new stacks and AI techniques. Add your own to the EXPERIMENTS list in the script.
Open to lead engineering, forward deployed, full-stack and AI engineering roles. Email is the fastest way to reach me.