shehryar ashraf

shehryar ashraf

Senior AI Developer / 6.2+ Years

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About

Senior AI Developer and Machine Learning Engineer with 6 years of experience taking AI systems from mathematicalmodelling to production across generative AI, agentic systems, natural language processing (NLP), computer vision andoptimization. Currently building retrieval-augmented generation (RAG) and multi-agent products for UK legal, fintech andmortgage clients. Previously led a real-time voice AI platform (950 ms p95 latency), fine-tuned an 8B large language model(LLM) for text-to-SQL (35% to 88% accuracy), and was sole architect of a solar-site optimization engine for a US client.Owns pipelines end to end, from modelling and implementation to AWS deployment and monitoring, and mentors theengineers around them.

Skills

Large language models (LLMs) retrieval-augmented generation (RAG) prompt engineering LLM fine-tuning (Llama) OpenAI API AWS Bedrock Google Vertex AI Azure AI vector databases embeddings LangGraph LangChain CrewAI Model Context Protocol (MCP) multi-agent systems tool use agentic workflows ElevenLabs Pipecat Deepgram Speechmatics real-time conversational agents PyTorch TensorFlow scikit-learn OpenCV Transformers (DETR) deep learning natural language processing (NLP) NumPy pandas Seaborn convex optimization vectorization parallel computing AWS (SageMaker Lambda Glue Bedrock) GCP (Vertex AI) Docker CI/CD (GitLab) Git FastAPI REST APIs Streamlit Apache Spark MongoDB Microsoft Presidio (PII redaction) GDPR compliance Python MATLAB C++ SQL OpenAI API AWS Azure
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Professional
Summary
6.2+ Years
October 2025 - April 2026
Associate Team Lead
Devsinc
August 2024 - October 2025
Data Scientist
A1 Strategez
April 2020 - June 2024
Math/Algorithms Expert
Zigron Pakistan
June 2026 - Present
Senior AI Developer
Consultancy Outfit (CO-UK)
October 2025 - April 2026
Associate Team Lead - AI
Devsinc
April 2020 - June 2024
Math / Algorithms Expert
Zigron
💼 Work Experience
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Applications &
Tools Known
AWS AWS
GCP GCP
Docker Docker
CI/CD CI/CD
Git Git
FastAPI FastAPI
REST APIs REST APIs
Streamlit Streamlit
Apache Spark Apache Spark
MongoDB MongoDB
Microsoft Presidio Microsoft Presidio
github github
gitlab gitlab
apache apache
Work History
Associate Team Lead
Devsinc
October 2025 - April 2026
Data Scientist
A1 Strategez
August 2024 - October 2025
Math/Algorithms Expert
Zigron Pakistan
April 2020 - June 2024
Senior AI Developer
Consultancy Outfit (CO-UK)
June 2026 - Present
• Loophole Larry (UK legal AI): Building the RAG pipeline for an AI case solver over UK law, where a user submits acase and the system generates a legal strategy. Own the data pipeline, knowledge-base indexing and retrievaloptimization for long-document queries: a single “query” is a case file running to hundreds of pages rather than ashort prompt, which rules out standard RAG retrieval patterns. Work in progress as more data is ingested.
• Data privacy: Implemented PII anonymization and redaction of case data with Microsoft Presidio to meet UK GDPRrequirements before documents enter the index.
• CossmoneyX and CurrencyGram (fintech): Designed and pitched to the CEO a 13-agent compliance system thatassesses readiness for FCA registration (crypto-asset for CossmoneyX, e-money for CurrencyGram): scrapes theregulator’s published requirements, indexes them alongside the company’s own policies in a vector database, andruns 11 parallel analyst agents (AML, sanctions, Travel Rule, financial promotions, etc.) plus a coordinator and areviewer agent to produce a decision-ready readiness report with strengths, gaps and suggested improvements.
• Property Peak (UK mortgage platform): Scoped and prioritised nine AI agents (case tracking, summarisation,handoff checks, ticket triage, onboarding, commission updates) across six user roles, each with role-based dataaccess and GDPR constraints; building them as LangGraph agents behind FastAPI REST APIs with platform dataexposed via the Model Context Protocol (MCP).
Associate Team Lead - AI
Devsinc
October 2025 - April 2026
• Voice AI: Led the design and development of a real-time conversational video/voice AI agent with Speechmatics andDeepgram speech-to-text, RAG for grounded answers and LangGraph agents for multi-step dialogue.
• Latency: Migrated the stack from a Pipecat telephony prototype to the ElevenLabs API as the product moved tovideo, bringing end-to-end response latency to a consistent 950 ms p95; replaced a custom LangGraph web-searchtool with OpenAI’s built-in web search, which was simpler to integrate and faster.
• Deployment: Deployed the platform on AWS and set up production monitoring.
• Mentoring: Trained and mentored junior and intern data scientists on LLM application development, code reviewand deployment practices.
Math / Algorithms Expert
Zigron
April 2020 - June 2024
Solar Power Generation and Optimization - Terrasmart (US)
• Sole developer and architect of an end-to-end engine that models a solar site and optimizes its energy production;reported directly to the Director.
• Built a 3D mathematical model of site layout, sun position over discrete time steps and inter-row shading betweenpanels, then optimized panel tilt angles to maximize total daily energy output.
• Solved the constrained optimization with MATLAB Global Optimization Toolbox solvers (pattern search, particleswarm, genetic algorithm) and tuned their parameters to speed up convergence.
• Reduced runtime by vectorizing the pipeline, parallelizing it with the Parallel Computing Toolbox and porting hotpaths to C++.
AI Dentistry - Automated Analysis of Dental X-rays
• Sole ML developer (initially); demonstrated the viability of AI for tooth numbering, dental diagnosis and bone-lossestimation from radiographs.
• Processed and annotated a dataset of several hundred dental images; achieved 90% accuracy on tooth numbering.
• Ran R&D on model architectures including DETR (DEtection TRansformer) and on preprocessing such as CLAHEand wavelet transforms to improve detection on low-contrast X-rays.
• Deployed the solution on a local server and presented the model’s capabilities and limitations to academics atUniversity College Dublin.
Major Projects
8★Projects
Peak Yield-> https://www.terrasmart.com/products/ground-mount/peakyield/
architected the solar power mathematical modeling + optimization algorithm as the sole developer
Dentasmart ->https://dentasmart.ai/
demonstrated the viability of using AI/ computer vision to automate dental tasks such as caries detection and tooth numbering
took the project ito prodcution
ChippyGPT-> a backend system utilizing openai api but using our prompts, our guardrails, and our databases, customized to fit our electric chip related issues
Loophole Larry-https://loopholelarry.co.uk/
a current project in progress, using agentic RAG to solve legal cases
🚀 Major Projects
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Education
MS in Electrical Engineering
LUMS
Graduated:Dec 2019
BS in Electrical Engineering
LUMS
Graduated:2015
BEACONHOUSE SCHOOL
Graduated: 2011
MS Electrical Engineering
Lahore University of Management Sciences (LUMS) Lahore, Pakistan
2019
BS Electrical Engineering
Lahore University of Management Sciences (LUMS) Lahore, Pakistan
2015
🎓 Education
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From the AI interview

shehryar ashraf answered

How shehryar ashraf talks about the work, from the spoken round of the AI interview and the written answers alongside it.

I took full responsibility for the Smart project as the sole developer, coding everything myself. We had weekly meetings with the director to discuss progress and outline the next steps, ensuring accountability throughout.
In the current project, we have a diverse team including a project manager and team lead. We follow a structured approach with a list of objectives, ensuring each part, like ML tasks, is properly managed and integrated.
I prioritize tasks by assessing what requires immediate attention and tackling the easiest tasks first before moving on to more complex ones, similar to how I approach exams.
We faced pressure deciding on which database to use. I created a document highlighting the pros and cons of each option to facilitate a decision.
Traditional image processing uses less memory but can't solve complex issues that require larger models. Memory requirements and problem complexity are key factors to consider.
Training a GAN involves a min-max balance; if the generator is too good, the discriminator fails, and vice versa, affecting the GAN's effectiveness and stability during training.