Services
AI and data engineering, designed and delivered by one accountable engineer.
I work with startups, product teams and enterprises that need an AI feature, a data pipeline or a backend built properly — and want a single person who owns it from architecture to deployment.
Service 01
Generative AI Development
LLM applications that do a specific job well — grounded in your data and measured against real examples.
What I deliver
- Custom LLM applications
- RAG implementations
- Enterprise knowledge assistants
- Document intelligence
- LLM integrations
- AI-powered automation
Problems solved
- Staff spend hours reading documents to find a handful of facts.
- Knowledge is scattered across files and nobody can find the answer.
- A chatbot prototype works in a demo but cannot be trusted with real questions.
Typical deliverables
- Working application with API
- Evaluation set and accuracy report
- Prompt and configuration repository
- Deployment and handover documentation
Technologies
- Python
- Azure OpenAI
- Anthropic Claude
- LangChain
- Vector databases
- FastAPI
Service 02
Agentic AI Engineering
Agents and tool-calling systems with clear boundaries, approval steps and full traceability.
What I deliver
- AI agent development
- Multi-agent systems
- MCP integrations
- Tool-calling architectures
- Intelligent workflow automation
- Human-in-the-loop systems
Problems solved
- A multi-step process needs judgement at some steps but is otherwise routine.
- Your team wants an assistant that can act on internal systems, safely.
- An agent prototype loops, costs too much, or cannot be audited.
Typical deliverables
- Agent service with typed tools
- Permission and approval design
- Run traces and evaluation harness
- Operating guide
Technologies
- Python
- Model Context Protocol
- Tool calling
- LangChain
- FastAPI
- Tracing & evaluation
Service 03
Data Engineering
Pipelines that are correct first and fast second, with quality checks built in from the start.
What I deliver
- ETL / ELT pipeline development
- Data ingestion
- Data transformation
- Data quality frameworks
- Databricks solutions
- Cloud data pipelines
- Medallion architecture
Problems solved
- Reports disagree because data is transformed differently in different places.
- A migration between databases keeps surfacing integrity problems.
- Pipelines fail silently and bad data reaches the business.
Typical deliverables
- Production pipelines with tests
- Data quality rules and validation gates
- Lineage and schema documentation
- Runbook for operations
Technologies
- PySpark
- AWS Glue
- Databricks
- Apache Airflow
- PostgreSQL
- Azure Data Factory
Service 04
Python Backend Development
Clean, typed FastAPI services and integrations — the layer that turns a model into a product.
What I deliver
- FastAPI development
- Backend architecture
- REST API development
- MongoDB and PostgreSQL integrations
- Microservices
- Performance optimization
Problems solved
- An AI feature exists as a notebook and needs to become a service.
- Systems need to talk to each other and the integration is fragile.
- An API is slow or hard to change safely.
Typical deliverables
- Documented REST API (OpenAPI)
- Test suite and CI pipeline
- Container images and deployment config
- Authentication and access control
Technologies
- Python
- FastAPI
- PostgreSQL
- MongoDB
- RabbitMQ
- Docker
Service 05
AI Product Development
From an idea to a deployed MVP: architecture, backend, AI layer, a usable front end and cloud deployment.
What I deliver
- MVP development
- AI SaaS prototypes
- End-to-end AI applications
- Backend and frontend integration
- Cloud deployment
- Technical consulting
Problems solved
- You have a product idea and need a first version in front of users.
- You need one engineer who can own the whole stack rather than coordinate several.
- A prototype needs to be rebuilt properly before it can take real traffic.
Typical deliverables
- Deployed MVP
- Source code and architecture notes
- Cost and scaling estimate
- Roadmap for the next phase
Technologies
- Python
- FastAPI
- LLM APIs
- Streamlit / web front end
- Docker
- AWS / Azure
Service 06
AI & Data Engineering Consulting
An experienced second opinion before you commit budget — or when something is not working.
What I deliver
- Architecture reviews
- Technical feasibility assessments
- AI adoption strategy
- RAG architecture recommendations
- Data pipeline optimization
Problems solved
- You are unsure whether a use case is realistic with today's models.
- A RAG system returns poor answers and the cause is unclear.
- Pipeline cost or run time has grown beyond what is reasonable.
Typical deliverables
- Written assessment with findings
- Recommended architecture and options
- Risk and effort estimate
- Follow-up working session
Technologies
- Architecture review
- Evaluation design
- Cost modelling
- RAG diagnostics
- Spark tuning
Full lifecycle
From business problem to delivered product
The value of working with one engineer is that nothing is lost between stages.
- 01
Business problem
Clarify the outcome, the users and what 'good' looks like.
- 02
Architecture
Choose the simplest design that meets the requirement.
- 03
AI engineering
Prompts, retrieval, agents and evaluation built together.
- 04
Backend & APIs
Typed, tested Python services around the model.
- 05
Data pipelines
Reliable ingestion, transformation and quality gates.
- 06
Testing
Regression sets for both code and model behaviour.
- 07
Deployment
Containers, CI/CD, observability and cost tracking.
- 08
Delivery
Documentation, handover and a system your team can run.
Working together
How an engagement runs
- 1
Conversation
A short call or email exchange to understand the problem, constraints and timeline.
- 2
Proposal
A written scope: what will be built, how it will be measured, timeline and cost.
- 3
Build
Work in short iterations with regular demos, so you see progress and can steer.
- 4
Handover
Deployment, documentation and a walkthrough so your team can own what was built.
Questions
Common questions
Do you work with international clients?
Yes. I am based in Hyderabad, India (IST) and work remotely with clients in other time zones, with overlapping hours agreed at the start.
How are projects priced?
Either a fixed price for a clearly scoped deliverable, or a time-based arrangement for open-ended work. The proposal states which, and why.
Can you sign an NDA?
Yes. Confidentiality is standard. This is also why enterprise work on this site is described without naming clients.
What if we are not sure AI is the right answer?
That is a good reason to start with a feasibility assessment. Sometimes the right recommendation is a simpler, non-AI solution, and I will say so.
Have an AI or Data Engineering challenge? Let's turn it into a working product.
Taking on a limited number of AI and data engineering projects. Typical reply within 2 business days.