GO105 – LLM Specialist & Machine Learning Engineer

🎯 Key Search Criteria

• 💰 Salary Expectation: €113,000+

• 📍 Location Preference: Happy to relocate to the US (San Francisco, Palo Alto) or Europe (Dublin)

• 🤝 Engagement Preference: Open to Permanent roles

• 🏭 Primary Industry Expertise: Healthcare Technology, Enterprise AI, E-commerce, FinTech (Web3/DeFi)

• 🧭 Core Value/Leadership Style: Bridge Research-to-Production, Multi-Agent System Orchestration, Production-Ready Scalability

• 🎓 Education Level: Master of Science (MS) in Applied Data Science

🚀 Motivation for New Role

• Seeking a new scaling challenge, applying deep expertise in Multi-Agent Systems and LLM-driven automation to a high-impact, global product.

• Desiring a role focused on bridging the gap between cutting-edge AI research (e.g., QLoRA, DPO fine-tuning) and reliable, production-ready engineering.

• A commitment to embedding advanced RAG and agent orchestration frameworks (LangGraph, LangChain) within a forward-thinking technology company.

Professional Summary

A Senior Machine Learning Engineer and LLM Specialist with nearly a decade of deep, hands-on expertise in designing and deploying complex Multi-Agent Systems, RAG-based pipelines, and LLM-driven automation workflows across high-growth technology sectors, including healthcare and enterprise AI. This executive is highly adept at building agent orchestration frameworks using cutting-edge tools like LangGraph, LangChain, and LangSmith, integrating real-time tracing, evaluation, and ensuring production-ready scalability.

With a proven track record, they effectively bridge research and engineering through fine-tuning open-source LLMs (QLORA, DPO) and consistently delivering reliable, high-impact AI products from concept to production. Possessing a comprehensive skill set across AI/ML, MLOps, Data Engineering, and Cloud Infrastructure (AWS/Kubernetes), this individual is ready to lead the development of the next generation of intelligent systems.

Core Competencies

• AI & Machine Learning: LLMs, RAG Pipelines, LangChain, LangGraph, LangSmith, Ragas, NLP, Deep Learning, QLORA, DPO, A/B Testing

• Data Engineering: ETL, Apache Spark, Apache Kafka, Apache Airflow, Snowflake, SQL, Big Data Processing

• MLOps & DevOps: MLflow, Kubeflow, MLOps, AWS SageMaker, AWS Lambda, Docker, Kubernetes, Terraform, GitHub Actions, CloudWatch

• Backend & Cloud: AWS (EC2, EKS, S3), Serverless Architecture, Elasticsearch, FastAPI

• Full Stack: React, Google Firebase, REST APIs, WebSocket Integration

• Specialised Analytics: Blockchain & DeFi Protocols (Uniswap, Aave, Lido), On-chain Data Processing

Key Achievements

💼 AI System Design & Agent Orchestration

• Led the end-to-end development of healthcare AI systems—from data exploration and model research to deployment—designing agentic AI workflows and collaborating with customers and stakeholders to align solutions with clinical and business objectives at a US-based analytics firm.

• Developed advanced Multi-AI Agent Systems integrating LLMs, RAG-MCP pipelines, and custom reranking models, ensuring accuracy and scalability through performance evaluation using Ragas and A/B testing.

• Built a scalable AI-driven research platform leveraging LangGraph, dual-LLM integration (including Gemini and GPT), and FastAPI-WebSocket architecture to automate company intelligence gathering and structured report generation.

• Optimised agent orchestration workflows using LangChain and LangSmith, achieving real-time tracing, evaluation, and observability across complex RAG-based pipelines.

• Fine-tuned open-source LLMs with QLoRA via Hugging Face Transformers, applying knowledge distillation to enhance model efficiency, alignment, and performance.

• Designed and deployed a Customer Support Chatbot integrated with a Neo4j Graph Knowledge Base, enabling dynamic, context-aware responses and graph-structured reasoning.

📈 Financial Impact & Operational Efficiency

• Developed and maintained a recommendation system on AWS using Elasticsearch at a major e-commerce platform, increasing customer engagement by 9% and boosting sales by 12%.

• Reduced fraudulent activity by 9% by designing and deploying a real-time anomaly detection system using traditional Machine Learning Algorithms for financial transactions at a US-based e-commerce firm, significantly improving security and asset protection.

• Engineered end-to-end ETL pipelines with PySpark to transform raw data into high-value features for recommendation and predictive modelling.

☁️ MLOps & Infrastructure Modernisation

• Implemented MLOps pipelines with Kubeflow, MLflow, and Seldon Core on EKS at a US-based e-commerce platform, automating model training, deployment, and monitoring for scalable production workflows.

• Deployed forecasting models via AWS SageMaker with canary deployment strategies and CloudWatch integration, reducing rollout risks and maintaining continuous uptime at a FinTech firm.

• Engineered a scalable, containerised microservices architecture with Docker and Kubernetes, accelerating deployment cycles and improving system resilience.

• Contributed to the successful migration of an E-commerce SaaS platform from a monolithic to microservices architecture, leveraging Apache Kafka for inter-service communication to enhance scalability and modularity.

• Implemented modern CI/CD pipelines using Docker, GitHub Actions, and Argo CD for automated deployments to EKS, enabling faster and more reliable release cycles.

• Managed infrastructure-as-code using Terraform, streamlining AWS resource provisioning and reducing deployment complexity and operational overhead.

🕸️ Web3 & Data Engineering

• Designed and developed an automated ETL pipeline using Snowflake, Apache Spark, and Kafka Streams APIs on AWS EC2 to ingest and process large-scale datasets from DeFi protocols such as Uniswap V3, Aave, and Lido.

• Implemented comprehensive data quality validation and monitoring systems to ensure accuracy, consistency, and reliability across all inputs for forecasting models.

• Developed a real-time crypto market intelligence dashboard powered by The Graph (Subgraph), delivering transparent on-chain analytics and actionable market insights.

Career History Overview (Anonymised)

• LLM Specialist & ML Engineer | Global Analytics Firm | 2.5 years

• Data & AI Engineer | Major E-commerce Platform | Under 2 years

• AI & Web3 Engineer | FinTech/Crypto Platform | Over 2 years

• Full Stack Developer | Enterprise Software Company | Under 2 years