Asad – Python, React, AWS

• Collinear AI builds the infrastructure and feedback loops that make LLMs safer, smarter, and more enterprise-ready.
• Its suite of products — Assess, Weaver, Guard, and Improve — cover the full lifecycle: generation → evaluation → safety → optimization → deployment. It is focused on fine-tuning, evaluating, and deploying Large Language Models (LLMs) for safe and high-performance use in enterprise environments.
• Its mission is to make LLMs more reliable, controllable, and domain-specific through continuous evaluation, reinforcement, and optimization loops.
• The company builds both developer-facing tools (SDKs, APIs) and enterprise products that integrate with existing data pipelines and infrastructure.

It was built for enterprise AI teams — specifically:

• AI/ML engineers fine-tuning and deploying foundation models in regulated industries (finance, healthcare, legal, pharma).
• AI safety & governance teams needing visibility into model behavior.
• Developers building internal copilots, RAG pipelines, or evaluation dashboards.

Example clients included large enterprises using private LLMs (GPT-4, Claude, or open-weight models like Llama-3, Mistral, etc.) and needing control, explainability, and compliance guarantees.

Main Features and Capabilities

• 🔍 LLM Evaluation Framework (Assess): Automated multi-criteria scoring for outputs.
• 🛡️ Real-time Safety Layer (Guard): Output filtering, PII detection, compliance enforcement.
• 🧩 Synthetic Data Generation (Weaver): Multi-agent pipeline to augment fine-tuning datasets.
• ⚙️ Continuous Fine-tuning (Improve): Feedback-driven retraining and reinforcement.
• 📊 Dashboard & Metrics: Factuality, hallucination rate, toxicity, and drift tracking.
• 🧠 Multi-model Support: Works with OpenAI, Anthropic, Hugging Face, or self-hosted LLMs.
• 🧰 Developer SDK + API: Python SDK, REST, and GraphQL APIs for automation.

• At Collinear AI, I led the design and implementation of our Agentic AI and LLM platform architecture spanning evaluation, fine-tuning, and deployment pipelines.
• I built and managed multi-agent systems that automated red-teaming, data synthesis, and evaluation using LLMs-as-judges.
• I also implemented Retrieval-Augmented Generation (RAG) workflows for factual grounding and contextual response evaluation across 100+ enterprise data sources.
• On the model side, I managed the fine-tuning and optimization pipeline using LoRA, QLoRA, and reinforcement fine-tuning (RFT) to continually improve our in-house and client-specific models.
• I designed and deployed scalable LLM inference services (FastAPI + vLLM/TGI) with monitoring, autoscaling, and cost tracking on AWS and GCP GPU clusters.
• Spearheaded the design, development, and deployment of the company's core Generative AI products, leveraging Python (FastAPI, PyTorch, Hugging Face Transformers, LangChain, MLflow) for backend and inference pipelines, and React.js, Next.js, TailwindCSS for scalable, user-friendly frontend interfaces.
• Championed CI/CD workflows using GitHub Actions, Docker, Kubernetes, Terraform, and Helm, and implemented infrastructure-as-code (IaC) practices to automate end-to-end deployments across AWS/GCP GPU clusters, optimizing resource allocation and reducing model training iteration time by 40%.
• Engineered a backend experimentation and orchestration framework for synthetic data generation, multi-model evaluation, retrieval-augmented generation (RAG) pipelines, and self-instruct tuning, enabling rapid prototyping and deployment of agentic AI copilots and autonomous workflows.
• Implemented advanced LLM red-teaming and jailbreak detection pipelines, utilizing LLM-as-a-Judge, Langfuse, and adversarial prompt injection frameworks to automatically evaluate safety, robustness, and alignment of generative models against real-world attack vectors.
• Collaborated cross-functionally with research, product, compliance, and design teams to deliver safe, explainable, and regulatory-compliant GenAI solutions, incorporating observability (OpenTelemetry, Prometheus, Grafana) and data privacy best practices for enterprise adoption.
• Overall, my strength was bridging AI research with production engineering — turning experimental LLM workflows into reliable, secure, and high-performance systems for enterprise deployment.

The company is a technology solution provider for the life sciences industry. They are a trusted partner for pharmaceutical, biotechnology and healthcare companies in the areas of Artificial Intelligence, Marketing Technology stack and Connected Health Solutions.

• Designed, developed, and delivered high-impact software solutions in healthcare and financial services, building scalable, secure applications with Java, Python, React, and AWS.
• Led AI-driven initiatives using Large Language Models (LLMs) for complex healthcare data management, including fine-tuning and deploying GPT-3, BERT, and Hugging Face models.
• Trained and optimized LLMs on custom datasets for domain-specific use cases, ensuring compliance, accuracy, and alignment with industry standards.
• Conducted model evaluation on accuracy, relevance, and performance, iteratively improving production readiness in sensitive environments.
• Partnered with stakeholders, product managers, and business analysts to align technical delivery with business goals.
• Contributed actively to Agile Scrum, code reviews, and process improvements, boosting team productivity and delivery timelines.
• Automated CI/CD pipelines and optimized cloud infrastructure using AWS, Docker, Jenkins, and Terraform (IaC) for faster, more reliable deployments.
• Delivered scalable AI/GenAI solutions across AWS (SageMaker, Bedrock), GCP (Vertex AI), and Azure (ML Studio), leveraging RAG pipelines, reinforcement learning, and prompt optimization for enterprise adoption.

The Loan Management System (LMS) project which leverages a diverse set of cutting-edge technologies within both the Java and Python ecosystems to deliver scalable and secure solutions.

• Architected and implemented a scalable Loan Management System (LMS) for Non-Banking Financial Companies (NBFCs), using Java, Python, AWS and React, capable of processing 10,000+ monthly loan applications, resulting in a 40% reduction in approval time and improved customer experience.
• Led a team of seven engineers in optimizing KYC verification workflows, automating identity validation processes by integrating third-party verification systems, which reduced manual intervention by 50% and improved compliance with regulatory standards.
• Integrated KYC modules and payment gateways within the LMS to streamline loan processing and disbursement, achieving a 50% reduction in overall processing time and increasing loan approval rates through enhanced system efficiency.
• Spearheaded the automation of loan disbursement workflows, enabling seamless integration with digital payment platforms, which improved loan recovery rates and reduced processing bottlenecks by 35%.
• Enhanced data security and scalability within the LMS, ensuring compliance with financial regulations and enabling the platform to easily scale with increased user and transaction volumes.

The matchmaking dating app with over 3 million active users within six months of launch.

• Led the design and development of a matchmaking app using Python & React.
• Developed and integrated RESTful APIs, third-party SDKs (Facebook, Truecaller), and enhanced backend services, improving user retention by 30%.
• Utilized Memcached and Redis to implement a caching mechanism that boosted app performance by 40%.
• Built scalable backend systems that supported high-volume transactions with 99.8% system uptime.