Asad – Python, React, AWS

The company helps enterprise customers build safe, reliable, and high-performing AI systems.

Their AI judge framework uses smaller, specialized reward models to evaluate LLM outputs for accuracy, safety, and alignment, turning real-world failure modes into high-quality post-training data. They combine rigorous post-training and alignment research with hands-on engineering to deliver automated evaluation, adversarial red teaming, and targeted model improvement, enabling enterprises to deploy AI they can trust.

• Spearheaded the design, development, and deployment of 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.

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.