Manchester, England, United Kingdom
Most growth-stage companies don’t struggle with ideas or demand they struggle with operational scale. As teams grow, systems remain manual, tools become fragmented, and execution slows down. I work with founders, CEOs, and leadership teams to design and implement AI-native systems that automate 30–60% of operational workload and remove bottlenecks across sales, support, and internal workflows. As Senior AI Consultant, I focus on architecting scalable SaaS platforms, autonomous AI agents, and secure cloud systems that drive real business growth. Across the systems I’ve led and delivered with my team: * 400+ global deployments * 120+ enterprise & growth partners * 10+ proprietary AI & SaaS products My role goes beyond technical delivery. I operate as a strategic partner, ensuring every system is aligned with measurable outcomes: faster execution, lower operational cost, and infrastructure that scales without increasing headcount. If you’re leading a growth-stage company and want to modernise operations with intelligent systems built for scale, I’m always open to the right conversation. 𝗟𝗲𝘁’𝘀 𝗧𝗮𝗹𝗸 💬 [email protected] 🗓 https://calendly.com/digimark-developers Artificial Intelligence | Generative AI | Automation | Machine Learning | Python | Data Engineering | Cloud (AWS, Azure, GCP) | NLP | Predictive Analytics
- Working in the Snowflake Infra team to build a scalable and robust data platform.
- Build and deploy AI and machine learning solutions in Python, including predictive models, NLP, and deep learning, to solve business problems and create new opportunities. - Design analytics and automation platforms that reduce manual work, speed up decision-making, and deliver measurable business results. - Lead AI projects from idea to production, mentor teams, set best practices, and ensure scalable, reliable solutions. - Work with business stakeholders to turn complex data into insights and automation that support growth and efficiency. - Develop robust software systems that are high-quality, scalable, and built for long-term performance. - Create and improve data architectures and pipelines on cloud platforms to power AI, analytics, and automation. - Guide teams in using modern AI tools and Python best practices to deliver practical, business-focused solutions.
Leadership and Collaboration: • Stepped in as team lead, overseeing project deliveries and sprints during manager's absence. • Promoted knowledge sharing through sessions on codebase module coupling and strategies for reducing coupling. • Mentored junior members to uphold high code quality standards and cultivate collaboration. • Led key projects like Schedule Metrics demonstrating end-to-end development expertise across multiple domains. • Provided comprehensive documentation that facilitated stakeholder buy-ins. • Engaged in detailed dev reviews and valuable design discussions, promoting a culture of quality and excellence within the team. DevOps • Contributed actively to implementing and enhancing automated CI/CD pipelines to optimise development processes. • Improved microservices via containerization, CI/CD, and container orchestration. • Led the development, change management, and documentation processes for implementing TimescaleDB, ensuring smooth integration into the system architecture. • Collaborated with the platform infrastructure team to implement infrastructure-as-code solutions for cloud infrastructure, using Terraform, Kubernetes, and Helm. Software Architecture • Developed configurable data integration service for faster client integrations. • Designed and implemented a resilient microservices architecture, achieving high availability and separation of concerns. • Resolved critical performance bottlenecks through strategic redesigns of architecture and execution flow of asynchronous system, enhancing system efficiency. • Led the implementation of storing time-series data to enhance query performance, automate processes, and streamline retention policy enforcement. Programming Languages • Utilised Golang for concurrency and performance in internal application. • Ensured Python best practices by conducting thorough development reviews and sharing knowledge.
• Ensured customer satisfaction by creating a robust backend system that met customer needs. • Improved API response times by implementing caching and async tasks with Celery and Redis. • Achieved high scalability and reliability by creating a strong cloud architecture with Docker and Google App Engine. • Automated cloud infrastructure setup with Terraform for easy deployment across various environments. • Established CI/CD pipelines with Bitbucket, saving development teams significant time annually.
• Designed ETL data pipeline following best data engineering practices with Google Cloud Composer (Managed Apache Airflow), boosting ETL process efficiency by 70%. • Utilised Google Dataproc (Managed Apache Spark/Hadoop) for data processing and BigQuery for loading processed data, establishing a data warehouse for analytics. • Designed informative dashboards for visual data analysis using Data Studio and matplotlib. • Automated data cleaning post-extraction with Google Dataprep and Google Cloud Functions (serverless), leveraging Python scripts with Pandas and Numpy. • Developed APIs for efficient management of Adword ads. • Employed shell scripting to automate manual tasks, enhancing workflow efficiency by saving time.