u/Alive-Tech-946

Hi — I’m an AI/ML and backend engineer with extensive experience building enterprise SaaS products from prototype to production. I’m available for contract or full-time consulting roles focused on data engineering, ML/AI systems, and end-to-end SaaS engineering.

What I do

- Design and implement robust data pipelines (ETL/ELT) and data warehouses, with PostgreSQL, pgvector, and vector DBs.

- Build scalable model serving and inference infrastructure (REST/gRPC, batching, async workers).

- Implement retrieval-augmented generation (RAG), fine-tuning, and prompt engineering workflows for production use.

- Integrate AI into product UX: search, recommendations, summarization, Q&A, and custom assistants.

- Architect cloud-native systems on AWS/Azure (Kubernetes, serverless, IAM, SSO), CI/CD, monitoring and cost optimization.

- Backend/frontend integration using Django, FastAPI, Next.js/React; message queues (RabbitMQ/Celery) and async processing.

- Security, compliance, and observability: access controls, logging, tracing, and model/data governance.

Recent highlights

- Launched a learning SaaS product — built core data stack, vector search, model orchestration, and client-facing AI features.

- Migrated monolith to microservices, introduced async task processing and autoscaling, reducing latency and cost.

- Implemented PG + pgvector-based semantic search for enterprise content with custom RAG pipelines and caching.

Looking for

- Contract, fractional CTO, or full-time consulting roles helping early-stage startups and growth-stage SaaS companies ship AI features and reliable data platforms.

- Projects that need architecture design, prototype → production delivery, or hands-on feature implementation.

Rates & availability

- Open to hourly, fixed-scope arrangements. Availability: short-term and ongoing engagements — let’s discuss timeline and budget.

Contact

- DM me here

I recently launched the beta of a product I’ve been building for a while, and I thought it might be useful to share some early numbers and lessons – especially for anyone else in the “lots of effort, no sales yet” phase.

Since launching beta:

1. I’ve taken a lot of demos from my ICP: I’m talking with the right people (HR, L&D, team leads), and the conversations are promising. They resonate with the problem, ask sharp questions, and give honest feedback. That’s been a huge morale boost even without revenue yet.
2. Don’t build too much before you get feedback: The features my early users care about most are not always the ones I assumed. A few things I spent weeks on are barely mentioned, while “simple” things like clearer reporting and smoother onboarding keep coming up. Now I try to ship smaller and talk to users more often before going deep.
3. Launch quickly and ship often: I’m glad I launched the beta when it still felt a bit “raw.” Waiting for perfect would have delayed all the learning I’m getting now. I’ve been iterating based on real calls, not just my own guesses.
4. Don’t quit (but do adjust): It’s mentally hard to see traffic and demos without closed deals (yet). I’m treating this as a signal: refine positioning, tighten the ICP, improve onboarding, and keep showing up. The only way to find what works is to stay in the game.

I recently launched the beta of a product I’ve been building for a while, and I thought it might be useful to share some early numbers and lessons – especially for anyone else in the “lots of effort, no sales yet” phase.

I’m building Semis, a platform for organisations to detect employee skill gaps, create role‑based learning paths, and track development in a way that actually connects to performance and retention. It’s aimed at managers, HR, and L&D folks who are tired of guessing who needs what training and living in spreadsheets.

Since launching beta:

1. I’ve taken a lot of demos from my ICP: I’m talking with the right people (HR, L&D, team leads), and the conversations are promising. They resonate with the problem, ask sharp questions, and give honest feedback. That’s been a huge morale boost even without revenue yet.
2. Don’t build too much before you get feedback: The features my early users care about most are not always the ones I assumed. A few things I spent weeks on barely get mentioned, while “simple” things like clearer reporting and smoother onboarding are what keep coming up. Now I try to ship smaller and talk to users more often before going deep.
3. Launch quickly and ship often: I’m glad I launched the beta when it still felt a bit early but ready. Waiting for perfection would have delayed all the learning I’m getting now. I’ve been iterating based on real calls, not just my own guesses.
4. Don’t quit (but do adjust): It’s mentally hard to see traffic and demos without closed deals (yet). I’m trying to treat this as a signal: refine positioning, tighten the ICP, improve onboarding, and keep showing up. The only way to find what works is to stay in the game.

https://preview.redd.it/am7j3nzaqn0h1.png?width=928&format=png&auto=webp&s=d49ab459e0cbc36c7f854ae49aa67f74af64f5ea

I’ve attached a snapshot of my traffic stats so you can see the reality behind the words – nothing viral, just steady conversations and slow compounding.

If you’re a manager, in HR/L&D, or responsible for training and development in your org and any of this resonates, I’d love for you to:

• Take a quick look at Semis, or
• Tell me what would make a tool like this a “must have” for you instead of a “nice to have.”

Happy to answer questions about the build, the beta experience, or the go‑to‑market side.