• Architected end-to-end Python backends covering data ingestion, historical processing, chart generation, sentiment handling, local LLM integration, and REST APIs consumed by web and mobile dashboards.

• Built Go microservices for low-latency data ingestion and background processing, including live data fetchers, caching layers for frequently accessed metrics, and background workers that keep technical indicators and analytics current in production.

• Integrated a production-grade microservice layer using Scala into existing Python systems, computing technical indicators (RSI, MACD, moving averages, volatility, support/resistance, trend state) alongside alternative data aggregation from social, news, and NLP sentiments.

• Designed strict JSON contracts between polyglot services and local LLMs, defining structured per-entity payloads and typed response schemas covering stance, confidence, risk factors, and narrative summaries.

• Built Scala pipelines for prompt construction, LLM invocation, and response validation, exposing results via REST APIs and queues while enabling incremental migration from monolithic Python services to a microservice architecture.

• Implemented parameter-efficient fine-tuning workflows for open-source LLMs using domain-specific datasets, improving structured inference quality and response consistency within production analytics pipelines.

• Designed retrieval-augmented generation (RAG) pipelines integrating vector search, structured prompts, and local LLM inference to improve domain-specific reasoning and factual grounding in real-time analytics workflows.

• Implemented vector embedding pipelines and semantic search services enabling low-latency retrieval of relevant signals from large structured and unstructured datasets for downstream LLM inference and automated decision-support systems.

• Accelerated AI adoption through end-to-end engineering projects showcasing data ingestion, embedding generation, fine-tuning, and deployment pipelines for generative AI models on HP AI Studio

• Reduced inference latency by optimizing agentic RAG services with advanced retrieval strategies and intelligent batching techniques

• Enabled customer success at scale through reproducible technical assets including coded demos, tutorials, architecture diagrams, and presentation materials delivered at conferences and recorded sessions

• Demonstrated real-world AI value through live conference demonstrations and presentations highlighting practical implementations of agentic and generative AI workflows

• Develop and write technical assets such as code for POCs, white papers, and blogs for internal and external consumption

• Develop technical sales and partner enablement materials targeting developers across various channels (sales, partners, events)

• Assist in product launches, collaborating with cross-functional teams on messaging and goals

• Work with engineering, design, and other teams to define and refine early-stage products and key functionality

To read some of my blogs about the Druid/Impy analytics database platform, please visit: https://imply.io/developer/authors/?value=rick-jacobs

• Create in-depth technical assets (code, blogs etc.) to highlight specific functionality

• Develop AI/ML projects on trending topics to showcase Apache Druid/Imply real-time analytical capabilities

• Conduct detailed analysis to identify integrations and branding content that resonate with our customer base, enhancing user engagement

• Determine key functionalities to highlight in projects, prioritizing features that align with business objectives and user needs

• Gain in-depth knowledge of integration platforms to effectively utilize them in enhancing product capabilities

• Data engineering including preparation, pre-processing and transformation using Scala code

• Develop and implement code to achieve desired project outcomes, demonstrating proficiency in programming and technical problem-solving, and the ability to grasps unfamiliar technologies quickly. For example, I wrote the code and article for a blog entitled How to Build a Sentiment Analysis Application with ChatGPT and Druid which was garnered over 15,000 views.

• Create and publish blogs to showcase our product capabilities, engaging the audience with insightful content on our technological advantages

• To read some of my blogs about the Couchbase document database platform, please visit: https://www.couchbase.com/blog/author/rick/

• Showcasing company product capabilities to technical and non-technical audiences

• Responsible for benchmarking comparisons of company products versus competitive products

• Creating, presenting and recording competitive content and industry trends demonstrations

• Design, develop/code and present partner collaboration demonstrations

• Design, develop/code and present custom webinars

• Data engineering including preparation, pre-processing and transformation using Scala code

• Design, develop/code and present demonstrations to highlight specific functionality eg.

NLP (Natural Language Processing) webservice utilizing commercial and custom algorithms

Cloud-enabled, REST API based, microservices to perform various tasks such as augment

static data with data received from Machine Learning services, emailing services, natural

language services, etc.

• Write and publish datasheets, blogs and partner collaboration summaries

• Implement, config and execute internal YCSB benchmarks and present results

• Generate visualizations using Tableau and PowerBI