Top Tech Jobs & Startup Jobs

Build and deploy LLM-based agents for banking clients using LangGraph, LiteLLM, and Langfuse. Develop backend services, API integrations, guardrails, evaluations, observability, and production infrastructure on AWS. Collaborate with security, networking, DevSecOps, and business teams to resolve delivery issues. Create documentation and runbooks, transfer ownership to client teams, and improve reusable agent templates, deployment pipelines, and platform observability after each embedded engagement.

Industrialize, deploy, monitor, and scale machine learning models in production. Build training, inference, retraining, CI/CD, model tracking, and model-serving pipelines. Use MLflow for versioning and governance, Databricks for workflow orchestration, Docker and Kubernetes for deployment, and observability practices for reliable ML operations. Collaborate with data scientists, data engineers, and business stakeholders to deliver scalable, production-grade ML systems.

Lead technical direction for a platform team building modern backend and data-stack solutions for ML products. Provide technical leadership, evaluate architectures, unblock teams (backend or data stack), use AI tools in workflows, document decisions via ADRs/RFCs, and support modernization with legacy/on-prem constraints.

Industrialize, deploy, monitor, and scale machine learning models in production. Design training, inference, and retraining pipelines; build CI/CD workflows; manage MLflow tracking, registries, versioning, and governance; develop scalable model-serving APIs; orchestrate Databricks workflows; and containerize applications with Docker for Kubernetes deployment. Collaborate with data scientists, data engineers, and stakeholders to establish reliable MLOps architecture and operational practices.

Build and optimize batch and streaming data pipelines, semantic models, and transformations on Databricks using Python and Spark. Define data contracts, quality rules, and data SLOs for data products. Guide domain teams on data modeling and product boundaries, mentor engineers and analysts, and improve platform templates, tooling, and engineering practices.

The Data Engineer will design and implement ETL processes, optimize data queries, automate pipelines, and develop scalable data infrastructures.

The Data Engineer Lead will oversee team collaboration, lead projects, design ETL processes, mentor members, and optimize data infrastructure.

As a Senior Data Engineer, you will develop batch and streaming data pipelines, maintain data contracts, and guide domain teams on data modeling, while mentoring other engineers.

The Data Developer will implement data pipelines, research new tools, and collaborate with teams for smooth integration of data systems.

As a Data Engineer Lead, you will develop batch and streaming data pipelines, maintain data contracts, guide teams on data modeling, and mentor engineers.