Hiflylabs

• Provide technical leadership post-sales to guide strategic customers in designing and implementing big data projects, from architectural design to data engineering and model deployment. • Identify and drive new initiatives that help customers turn their data into actionable business value, aligning these initiatives with their business goals for continued success. • Architect production-level workloads, including performance testing and optimization of end-to-end pipeline loads. • Deliver tutorials and training sessions to promote community adoption (including hackathons, conference presentations, etc.).

• Strategic Roadmap Ownership: Define the "Why" and the "What" for our Databricks-based projects. • Partner with stakeholders to prioritize high-ROI AI features. • Requirements Gathering and User Stories: translate complex stakeholder needs into actionable user stories and technical requirements. • Business Logic Interpretation: help technical counterparts understand complex business logic and translate it into a technical context. • Cross-Functional Orchestration: Lead regular syncs between the engineering team and key stakeholders at the customer, ensuring that technical delivery stays aligned with business strategy. • Testing and Quality Assurance: Lead QA (Quality Assurance) and UAT (User Acceptance Testing) of features developed by technical team while maintaining a feedback loop to ensure stakeholder-developer alignment. • Identify additional opportunities at the customer and build a strong relationship with the key stakeholders.

• Design and implement scalable AWS cloud architectures with a strong focus on networking and platform stability • Architect and manage AWS networking components (VPCs, routing, egress control, hybrid connectivity) • Build and maintain Databricks platform architecture on AWS, ensuring best practices and scalability • Develop and manage Infrastructure as Code (IaC) using Terraform, with a focus on reusable and modular design • Define and implement cost allocation models across tenants and projects • Design and develop CI/CD pipelines (e.g., Declarative Automation Bundles – DAB) to enable automated deployments • Collaborate with internal stakeholders to define platform standards, governance, and best practices • Drive automation initiatives to reduce manual operations and improve reliability

• Define the "Why" and the "What" for our Databricks-based projects. • Partner with stakeholders to prioritize high-ROI AI features. • Translate complex stakeholder needs into actionable user stories and technical requirements. • Help technical counterparts understand complex business logic and translate it into a technical context. • Lead regular syncs between the engineering team and key stakeholders at the customer, ensuring that technical delivery stays aligned with business strategy. • Lead QA (Quality Assurance) and UAT (User Acceptance Testing) of features developed by technical team while maintaining a feedback loop to ensure stakeholder-developer alignment. • Identify additional opportunities at the customer and build a strong relationship with the key stakeholders.

• Own the architecture and delivery of production-grade LLM systems and classical ML solutions • Design, evaluate, and optimize RAG pipelines (retrieval strategy, chunking, indexing, monitoring) • Build scalable, production-grade LLM services and agentic workflows, alongside traditional ML systems where appropriate • Define architecture trade-offs (LLM vs traditional ML, fine-tuning vs RAG, hosted vs self-managed models), with a strong focus on system-level optimization (latency, cost, scalability, reliability) • Architect and optimize distributed GenAI and ML workloads on Databricks (Spark, MLflow), leveraging deep understanding of the platform ecosystem • Implement evaluation frameworks to measure quality, hallucination, and performance • Productionize systems with proper CI/CD, monitoring, rollback, and versioning • Independently design AI solutions tailored to client problems, translating business needs into scalable architectures • Lead technical decisions in client engagements and actively contribute to pre-sales architecture discussions • Mentor team members and define GenAI and ML best practices.

• Collaborate with cross-functional teams (developers, product managers, stakeholders) to create user-centered designs that meet business goals and user needs • Design intuitive and visually appealing interfaces for web and mobile applications, ensuring a seamless user experience • Conduct user research, competitive analysis, and usability testing to inform design decisions • Create wireframes, prototypes, and high-fidelity mockups to communicate design ideas effectively • Iterate on designs based on feedback from users and stakeholders in an agile environment • Ensure design consistency with brand guidelines while staying current on design trends and best practices • Work closely with developers to ensure designs are accurately implemented and functional • Stay up to date with the latest UI trends, techniques, and technologies

• Take part in various partner projects, building end-to-end web solutions from UI to backend • Lead and contribute to development efforts across the full stack, supporting technical decision-making • Design and implement web applications using modern frameworks and best practices • Translate business and user needs into intuitive, scalable technical solutions • Collaborate closely with designers, customers, and fellow engineers to ensure alignment • Plan, estimate, and implement development tasks based on designs and functional requirements • Ensure timely delivery of features that meet both technical and user expectations • Write and maintain unit, integration, and end-to-end tests across the stack • Identify, debug, and resolve issues to maintain high quality and performance • Support team collaboration through clear task ownership and structured workflows

• Strategic Roadmap Ownership: Define the "Why" and the "What" for our Databricks-based projects. Partner with stakeholders to prioritize high-ROI AI features. • Requirements Gathering and User Stories: translate complex stakeholder needs into actionable user stories and technical requirements. • Business Logic Interpretation: help technical counterparts understand complex business logic and translate it into a technical context. • Cross-Functional Orchestration: Lead regular syncs between the engineering team and key stakeholders at the customer, ensuring that technical delivery stays aligned with business strategy. • Testing and Quality Assurance: Lead QA (Quality Assurance) and UAT (User Acceptance Testing) of features developed by technical team while maintaining a feedback loop to ensure stakeholder-developer alignment. • Identify additional opportunities at the customer and build a strong relationship with the key stakeholders.

• Help us build impactful data products! • Build real stuff on a cloud data platform: contribute to features and improvements across ingestion, transformation, and serving layers • Create & maintain ELT pipelines: bring new data in, transform it reliably, and keep it running as requirements evolve • Turn business questions into dashboards: collaborate with business stakeholders to create and improve reports and visualizations • Help keep these platforms healthy: support deployments, monitoring, and troubleshooting (you’ll learn how “production” really works)

• Discovering, understanding, overviewing, and proposing solutions to clients’ business problems that can be solved on the basis of data analytics and visualization • Assessing, coordinating the clients dashboarding requirements, and proactively proposing dashboard, KPI and information hierarchy that fits the business questions • Designing and developing executive and tactical (rarely operational) dashboards • Enhance our Clients’ reporting capabilities through reporting governance and knowledge transfer of best practices • Through data analysis uncover business opportunities and problems followed by formulating and conveying recommendations • Supporting of data analysis projects with presentation of the results of exploratory analyses and visual presentation of data analysis results • Creating the semantic models behind the dashboard as needed