Top Tech Jobs & Startup Jobs

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.

Designs enterprise-scale data and analytics architectures using Azure, Snowflake, and Databricks. Assesses legacy platforms, develops modernized end-to-end data ecosystems, and establishes data modeling, pipeline, security, governance, privacy, and compliance standards. Partners with senior clients and technology teams to define solutions, lead architecture reviews, guide distributed engineering teams, and drive modernization through automation and reusable frameworks.

Administer and support enterprise Snowflake environments, including security, access controls, performance, monitoring, troubleshooting, production releases, and platform stability. Support DataOps and DevOps processes, build CI/CD pipelines, automate administrative tasks with Python, SQL, and shell scripting, and maintain data ingestion and integration operations.

Build and deploy production-grade Generative AI and Agentic AI solutions for enterprise clients. Responsibilities include designing LLM applications, RAG systems, AI agents, and multi-agent workflows; integrating enterprise data and applications; deploying and scaling cloud-based systems; and optimizing reliability, security, performance, and cost. The role requires direct client engagement, strong software engineering skills, cloud expertise, and hands-on experience with LLM frameworks, agent orchestration, and production AI operations.

Develop and refactor Python optimization algorithms for supply chain, inventory allocation, replenishment, and pricing optimization. Build predictive models, analyze large datasets, design experiments, monitor KPIs, and provide data-driven recommendations. Collaborate with business stakeholders and data science teams to deliver analytical solutions, while coaching colleagues and applying advanced statistical and operations research methods.

Designs, develops, and maintains scalable Power BI dashboards, semantic models, and reporting infrastructure. Creates optimized DAX measures and time-intelligence logic, transforms data with Power Query, improves report performance, and implements RLS and OLS security. Collaborates with data engineers, analysts, data scientists, AI teams, and business stakeholders to deliver actionable analytics solutions.

Lead advanced analytics initiatives for operations, including lakehouse data integration, maintenance and work-order analytics, asset reliability modeling, inventory forecasting, anomaly detection, and NLP of technician notes. Build Databricks Apps and production ML workflows using Python, PySpark, SQL, MLflow, and Databricks technologies. Partner directly with operations stakeholders to translate business knowledge into data definitions and self-service analytical tools.

Designs, builds, and maintains scalable ETL/ELT pipelines, cloud data infrastructure, storage, and batch or real-time processing workloads. Optimizes queries, PySpark jobs, pipelines, performance, and cloud costs. Partners with BI, analytics, data science, and AI teams to develop dimensional data marts supporting reporting and machine learning. Implements governance, data quality validation, lineage tracking, security controls, and automated infrastructure and code deployments using CI/CD practices.

Lead transaction monitoring optimization for a major bank by analyzing historical alerts and transactions, developing classification and logistic regression models, replacing broad rules with risk-based segmentation, and recommending precise monitoring scenarios. Serve as the primary onshore client contact, coordinate offshore analysts, ensure alignment with model risk governance, prepare validation documentation, and communicate findings to technical and business stakeholders.