Melih – Python, AWS, AI agent development

A global IT engineering and consulting platform that provides digital transformation services, custom software development, and specialized staffing. The role involved designing and implementing a code generation agent.

• Architected and implemented agentic workflows for simulation-code generation, designing document-to-code pipelines that translated structured and unstructured inputs into executable outputs through multi-step LLM orchestration.
• Built and evaluated code-generation agents using custom agentic architectures, comparing performance, reliability, and developer ergonomics against Claude Code SDK to guide tooling and workflow decisions.
• Developed MCP tools and reusable Claude skills to extend agent capabilities, improve task specialization, and enable more structured human-in-the-loop and automated execution flows.
• Leveraged LangChain and custom orchestration patterns to coordinate planning, tool usage, context handling, and multi-stage reasoning across backend AI workflows.
• Built production-oriented backend services with FastAPI to expose agent functionality through clean APIs, supporting integration, testing, and scalable deployment patterns.
• Developed frontend interfaces in React to enable interaction with agent workflows, document ingestion pipelines, and generated code outputs.
• Deployed and operated AI services on GCP, aligning infrastructure with low-latency execution, service reliability, and practical experimentation needs across evolving agentic systems.

A Stockholm-based talent marketplace that connects businesses with pre-vetted, remote software developers and tech professionals. The role involved architecting and deploying a multi-agent LLM architecture for an automated content generator.

• Architected and deployed a multi-agent LLM architecture for automated content generation, leveraging LangChain/LangGraph to orchestrate planner →agent → aggregator workflows. Incorporated advanced web scraping, search, and enrichment pipelines.
• Designed and scaled Retrieval-Augmented Generation pipelines combining Pinecone vector databases with Neo4j knowledge graphs to enable context-rich, real-time reasoning over complex data.
• Implemented custom inference pipelines tightly integrated with internal databases and deployed to AWS (EC2, Lambda, CI/CD) for low-latency, production-grade performance, empowering marketing teams with scalable post-generation workflows.
• Translated complex domain rules into machine-readable logic, creating structured representations for regulatory or policy-driven automation tasks. Built dynamic ETL pipelines for real-time synchronization between PostgreSQL and Neo4j.
• Fine-tuned and optimized generative models using PEFT and LoRA techniques to achieve domain-specific outputs with minimal resource overhead, suitable for regulated and high-precision use cases.
• Developed internal AI-powered enrichment tools for query handling, content summarization, document classification, and context-aware recommendation systems, accelerating knowledge discovery.
• Exposed robust LLM-based APIs for seamless integration into client-facing applications, emphasizing auth-first design, observability, and high availability.
• Added structured logging, tracing, and metrics for debugging and monitoring.
• Automated internal linking and relevance strategies, significantly improving SEO performance and content discoverability across digital platforms.
• Consistently operated with founding-tier ownership, translating ambiguous requirements into shipped features within days/weeks, aligning with startup execution velocity.

An AI platform focused on human intelligence and high-quality data used to train and improve frontier foundation models. The project involved architecting and developing a production-grade AI-powered recruitment chatbot.

• Architected and developed a production-grade AI-powered recruitment chatbot leveraging Retrieval-Augmented Generation.
• Implemented a scalable agentic RAG pipeline incorporating Neo4j knowledge graphs for complex contextual reasoning and compliance-like query understanding.
• Designed advanced memory management systems, separating short-term conversation context from persistent long-term memory.
• Integrated sentiment analysis and adaptive response strategies to dynamically adjust the chatbot’s tone and escalation behavior.
• Engineered and optimized vector database architecture using Pinecone, employing Matryoshka Representation Learning to minimize storage footprint while maintaining high retrieval precision.
• Built and maintained dynamic ETL pipelines to synchronize data in real time between PostgreSQL and Neo4j, ensuring the knowledge graph remained continuously updated and production-ready.
• Led the design and deployment of robust, cloud-native infrastructure (AWS/GCP) for agent orchestration and retrieval, using Docker and CI/CD pipelines to achieve rapid, reliable releases.
• Created a real-time recommendation engine clustering users based on behavioral data and feeding results back into the pipeline.
• Collaborated with ML engineers and product stakeholders to translate domain-specific logic into machine-readable control flows.
• Instituted observability practices including structured logging, tracing, and performance metrics to monitor latency (p50/p95) and throughput, proactively improving system reliability.

A conversational intelligence platform designed for enterprises to analyze and improve human-to-human interactions, specifically within call centers and sales organizations. It uses AI to monitor 100% of customer communications—including audio, chat, and email—to drive better business outcomes

• Created the architecture and implemented complex system pipelines utilizing Large Language Models (LLMs)
• Fintuned custom 10B+ LLM on bank specific dataset on multi-gpu system architecture.
• Implemented self-refining technique designed for the bank assistant tool.
• Enabled custom LLM to execute API calls, effectively retrieving necessary widgets and displays for the desktop application, thus enhancing system speed and performance.
• Integrated real-time processing and display capabilities in the desktop application, enhancing user experience through quicker response times.
• Used specialized knowledge in AI to design custom LLM-based pipelines, improving the quality and performance of our systems.
• Utilized innovative prompt engineering techniques to enhance the functionality and reliability of the AI systems.
• Integrated speech-to-text models for real-time transcription retrieval, streamlining communication and improving the overall performance of the bank assistant.
• Enabled LLMs to execute API calls, effectively retrieving necessary widgets and displays for the desktop application.
• Led the adoption of AI technologies in enhancing customer representative performance.
• Encouraged cross-functional collaboration among engineering, data science, and customer service teams.
• Led the development of a robust backend architecture to serve the desktop application.
• Provided regular updates to key stakeholders, including executive leadership and board members, on project progress, risks, and outcomes.
• Kept abreast with emerging technologies, market trends, and industry best practices.
• Led a team of engineers, fostering a collaborative and innovative work environment that promoted professional growth and knowledge sharing.

A data-driven technology company that uses an AI-powered platform to connect companies with remote software developers and tech talent from around the world.

• Prepared code based dataset and train a GPT model to enhance the coding ability of the LLM side of the conversation model. Tried different finetuning strategies.
• Contributed to the development of the most popular LLM-based chatbot model having GPT architecture.
• Designed and implemented efficient and scalable algorithms for pre-processing, training, and evaluation of large-scale deep learning models.
• Developed and optimized software components to enable high-speed inference and seamless integration with various production systems.
• Developed novel techniques for transfer learning in deep learning models, specifically applied to the LLM-based chatbot model, resulting in improved performance and faster convergence.
• Implemented advanced natural language processing algorithms to enhance the chatbot model's understanding and generation of code-related queries and responses.
• Optimized memory utilization and runtime efficiency of the chatbot model.
• Researched and implemented state-of-the-art techniques, such as attention mechanisms and transformer architectures.

A technology company that uses Artificial Intelligence to automate and streamline the dental industry's administrative and financial workflows.

• Prepared and trained custom transformer networks for tooth detection, segmentation, keypoint detection, and various regression/classification problems using Pytorch.
• Trained models on Torch Elastic, Kubeflow, and AWS EC2 instances.
• Developed and implemented computer vision algorithms to analyze dental X-rays and detect abnormalities, such as cavities, cracks, and fractures.
• Designed and deployed a data pipeline to collect, preprocess, and augment dental X-rays from multiple sources, including dental clinics and hospitals.
• Optimized the performance of deep learning models by experimenting with different architectures, loss functions, and hyperparameters.
• Conducted rigorous testing and evaluation of models to ensure their accuracy, robustness, and generalizability on different datasets.
• Experimented extensively with a variety of custom neural network architectures and loss functions, leading to significant advancements in model performance and specificity for dental imaging applications.
• Conducted in-depth analysis and testing of various neural network configurations, identifying optimal structures for accurate detection of dental issues.
• Worked on making the AI models faster without losing accuracy.
• Converted Pytorch models to OpenVINO models for optimizing the models for the production environment.