B2

• Develop and maintain scalable data pipelines (batch and streaming) • Ensure efficient ingestion, transformation, and provisioning of data • Model data to support analytics and AI applications • Integrate multiple data sources (APIs, event streams, and internal systems) • Build and optimize jobs using Spark / PySpark • Orchestrate pipelines using Step Functions • Work with event-driven architecture • Organize and structure data in Data Lake / Lakehouse environments • Implement data partitioning and optimization strategies • Ensure pipeline quality, reliability, and observability • Optimize cost and performance in AWS environments • Collaborate with data teams (analysts and data scientists) to deliver solutions • Support technical decisions related to data architecture and engineering

• Develop and lead machine learning and generative AI solutions • Design and implement applications using LLMs and agent-based architectures • Translate business problems into scalable analytical and AI solutions • Build and validate statistical and machine learning models • Apply advanced feature engineering techniques • Develop data pipelines in partnership with engineering • Implement RAG strategies and tool orchestration • Evaluate, monitor, and optimize model and LLM performance • Ensure quality, reliability, and continuous improvement of solutions • Work with multiple models, balancing cost and performance • Collaborate with engineering, data, and product teams • Communicate results and insights clearly to stakeholders.

• This professional will be responsible for turning data from the Data Lake into actionable insights, supporting strategic and operational decision-making. • Will work directly on identifying patterns, trends and opportunities, with close collaboration with engineering and AI teams. • Expected to take an analytical, investigative approach focused on generating business value, especially in a dynamic environment with potential for internal placement at the client. • Perform advanced exploratory data analyses on large volumes of data • Identify patterns, anomalies and opportunities in operational and strategic data • Define, monitor and evolve KPIs and business metrics • Build reliable metrics ensuring data consistency • Develop executive and operational dashboards to support decision-making • Translate complex data into clear insights for business teams • Collaborate with engineering and data science teams on structuring pipelines • Explore data in Data Lake environments using AWS tools • Apply AI-assisted analysis techniques and Large Language Models (LLMs) • Ensure best practices in visualization, data quality and data governance • Support decision-making based on structured, hypothesis-driven analyses

• Conduct end-to-end recruitment processes for technology roles • Perform active sourcing/hunting of candidates across various platforms • Conduct competency-based interviews and assess behavioral and technical fit • Ensure a positive candidate experience throughout the entire process • Manage multiple openings simultaneously with organization and agility • Maintain and track recruitment metrics • Support internal communications and employer branding initiatives • Assist in organizing employee campaigns and internal initiatives • Manage budget and support procurement of company promotional items • Work with resilience and proactivity in a highly competitive market