Top Tech Jobs & Startup Jobs

Define the vision, strategy, and roadmap for AI-powered products. Lead use-case evaluation, requirements, experimentation, development, deployment, adoption, and continuous improvement across Generative AI, LLM, RAG, and agentic solutions. Collaborate with engineering, data science, design, and business stakeholders; establish success metrics; manage AI risks; and communicate product value, progress, and strategy to leadership and clients.

Designs, develops, and maintains scalable Azure data pipelines and ETL/ELT solutions using Python, Scala, Spark, and Azure data services. Responsibilities include optimizing distributed processing, managing data quality and validation, monitoring and troubleshooting pipelines, supporting production deployments, and collaborating with architects, analysts, and cross-functional teams. The role also requires Hadoop experience, Control-M workflow orchestration, SQL performance tuning, and adherence to testing, version control, and CI/CD practices.

Develop and deploy machine learning and predictive models for banking and financial services clients. Responsibilities include feature engineering, model training and validation, credit and risk modeling, fraud detection, production-ready Python development, stakeholder engagement, MLOps collaboration, model monitoring, and communicating technical findings and business impact.

Own enterprise HR analytics and AI consulting engagements from discovery through delivery. Translate business needs into scalable analytics solutions, lead discovery workshops, manage senior client stakeholders, guide cross-functional data science and engineering teams, and support account expansion. Advise on HR metrics including headcount, attrition, turnover, span of control, and workforce productivity while embedding analytics into business processes.

Lead analytics consulting engagements by translating business objectives into analytical roadmaps, defining and governing KPIs, assessing data quality, and conducting hands-on data exploration using SQL and Python. Partner with stakeholders and Power BI developers to design metric structures, semantic models, wireframes, and dashboards that support strategic decision-making.

Design, develop, and maintain scalable data pipelines using Databricks, Spark, Python, Scala, and Hadoop. Build and optimize ETL/ELT processes, manage batch workflows with Control-M, troubleshoot failures, and implement data validation and quality checks. Support production deployments, performance tuning, incident resolution, and ongoing maintenance while collaborating with architects, analysts, and cross-functional teams.

Leads financial crime compliance business analysis across AML, KYC, transaction monitoring, sanctions screening, investigations, and regulatory reporting. Facilitates discovery, documents requirements, maps banking data, translates regulations into monitoring rules, evaluates vendor platforms, and manages UAT. Partners with compliance stakeholders, architects, developers, data engineers, and QA teams to support modernization initiatives, resolve functional questions, manage scope, and ensure effective alert generation with controlled false positives.

Manage enterprise BI consulting initiatives by gathering stakeholder requirements, designing data models and flows, communicating architecture, and overseeing Power BI dashboard delivery. Lead design reviews, data validation, governance, risk mitigation, compliance, issue resolution, and offshore collaboration. The role requires strong stakeholder management, enterprise reporting experience, and expertise with Power BI, with Alteryx and Tableau preferred.

Deploys, integrates, and scales enterprise Generative AI solutions across Azure environments. The role develops LangGraph agentic workflows, fine-tunes and evaluates LLMs, reduces hallucinations, integrates retrieval systems and APIs, and collaborates with customer engineering teams. Responsibilities also include requirements documentation, process mapping, automation engineering, software architecture, and communicating business value.

Leads financial crime compliance business analysis for AML modernization initiatives. Partners with compliance, risk, engineering, and IT teams to define requirements, map banking data, translate regulations into monitoring rules, evaluate vendor capabilities, conduct gap analysis, and manage UAT. Develops BRDs, FSDs, test plans, user stories, and acceptance criteria while supporting transaction monitoring, KYC, sanctions screening, investigations, case management, and regulatory reporting programs.