Jobs at Blackbuck Insights LLC

Lead enterprise architecture strategy for healthcare payer organizations, including AI solutions, API management, integration, security architecture, application rationalization, and technical-debt reduction. Translate business strategy into scalable architectural roadmaps, govern complex system designs, evaluate technology options, and lead improvement initiatives. Advise stakeholders, mentor architects and technical teams, facilitate design reviews, and ensure solutions align with enterprise standards, security, operational, and business requirements.

Develop proprietary foundation models, fine-tune domain-specific models, and architect multi-agent systems with autonomous reasoning, tool calling, and advanced RAG. Build curated and synthetic datasets for pretraining, alignment, and benchmarking. The role requires deep generative AI expertise, including LLM development, agent frameworks, foundation model architecture, and production-grade RAG pipelines, along with proficiency in modern model training, inference, vector infrastructure, and data technologies.

Develop and maintain Java and Spring Boot enterprise applications, REST APIs, and microservices on Google Cloud Platform. Deploy and troubleshoot cloud-native applications using GKE, Cloud Run, Docker, and Kubernetes. Integrate Pub/Sub, Cloud SQL, and Cloud Storage for messaging, persistence, and file storage. Diagnose performance and production issues, optimize databases, implement secure coding practices, and collaborate with architects, DevOps, QA, and development teams.

Develop and support robotic process automation solutions using UiPath Studio and Orchestrator. Build workflows with REFramework and queues, integrate automations with REST APIs, databases, and enterprise applications, and troubleshoot production issues.

Develop and customize Salesforce applications using Lightning Web Components, Apex, triggers, data modeling, custom objects, workflows, and formula fields. Integrate Salesforce with internal web applications and third-party platforms such as Amazon Connect and LivePerson. Apply object-oriented design, relational database knowledge, web services, and Agile Scrum methodologies in a collaborative software engineering environment.

Develop and maintain integrations using MuleSoft Anypoint Platform, Mule 4, and DataWeave. Build and manage RESTful and SOAP APIs using RAML and API Gateway, connect cloud platforms such as AWS, Azure, and GCP, and support CI/CD pipelines, Git, and DevOps practices. Apply strong problem-solving and communication skills to deliver reliable integration solutions.

Architect and govern Azure-based event-driven integration platforms and an AI-powered Snowflake data intelligence platform. Design APIs, routing, transformation, observability, security, semantic layers, RAG memory, inference, recommendation, and agent orchestration systems. Lead architecture workshops, roadmaps, estimates, documentation, stakeholder presentations, and engineering mentorship while ensuring compliance with financial services governance and security requirements.

Designs, builds, and optimizes scalable data pipelines, ETL/ELT processes, integrations, and cloud data platforms. Ensures data quality, governance, lineage, observability, reliability, and performance while supporting Meridian initiatives and AI/ML data pipelines. Collaborates with architects, analysts, product owners, and stakeholders; provides technical mentorship; participates in architecture and code reviews; and promotes automation, documentation, testing, and engineering best practices.

Entry-level engineering trainees will contribute across the software and data development lifecycle, including requirements analysis, architecture, coding, testing, deployment, documentation, and maintenance. They will gain hands-on experience with cloud, data engineering, LLMs, prompt engineering, autonomous AI agents, ETL/ELT pipelines, distributed data systems, APIs, CI/CD, and software quality practices. The role requires collaboration, problem-solving, communication, adaptability, and full-time work from the Schaumburg office.

Leads data discovery, profiling, quality assessment, and Source-to-Target Mapping for wealth management data platforms. Analyzes complex financial datasets using advanced SQL, documents business rules and transformations, translates requirements for ETL and data engineering teams, supports data governance and testing, and communicates findings to technical and business stakeholders.

Leads data discovery, profiling, quality assessment, and Source-to-Target Mapping for enterprise wealth management platforms. Analyzes client, portfolio, transaction, holdings, performance, and billing data using advanced SQL; documents business rules and transformations; partners with ETL and data pipeline engineers; supports data governance, validation, testing, and stakeholder communication.

Leads end-to-end machine learning research, including problem definition, experimentation, model development, evaluation, and production deployment. Develops advanced models and scalable training pipelines using modern frameworks, collaborates with ML Engineering, MLOps, Product, and Data Engineering, and defines datasets and labeling strategies. Mentors junior researchers, reviews technical work, establishes reproducibility standards, and contributes to AI strategy, roadmap planning, and team development.