Ambush

Backend Engineer

• Build and maintain Python services that orchestrate LLM agents
• Implement routing and delegation across multiple agents
• Implement agent-to-agent communication using the A2A protocol and agent cards
• Develop streaming APIs that deliver results incrementally
• Integrate LLMs through AWS Bedrock/Anthropic, Azure OpenAI, or comparable platforms using tool calling and prompt templates
• Instrument applications with observability and LLM tracing
• Troubleshoot issues in distributed environments
• Package and deploy services in containers and Kubernetes clusters
• Secure service-to-service communication using mTLS
• Manage the certificate lifecycle
• Contribute to CI/CD pipelines and version control best practices

Explore Senior Python Engineer, Agentic AI at Ambush. Review requirements and application details.

Senior Python Engineer, Agentic AI

AI Engineer

• Design, build, and deploy enterprise-grade GenAI and Agentic AI solutions for complex financial services workflows
• Build workflows for exception classification, triage, root-cause analysis, impact assessment, and remediation recommendations
• Develop agentic workflows with orchestration, tool/function calling, state management, and human-in-the-loop validation
• Correlate new exceptions with historical issues, root causes, business rules, and transaction/data attributes
• Build Python and SQL services to query, transform, and analyze large structured enterprise datasets
• Develop reusable tools and services for AI-agent retrieval, investigation, analysis, and workflow execution
• Integrate AI applications with enterprise APIs, databases, workflow/ticketing platforms, and internal data sources
• Apply RAG and context retrieval over regulatory documents, historical knowledge, and issue repositories
• Implement confidence scoring, validation, guardrails, and traceability for AI-generated outcomes
• Build backend services and APIs using Python/FastAPI and deploy them on a major cloud platform
• Implement testing, logging, evaluation, observability, and production engineering practices
• Collaborate with onshore and offshore engineers, architects, business analysts, data engineers, and application teams

Explore Machine Learning/AI Engineer at Ambush. Review requirements and application details.

Machine Learning/AI Engineer

Data Scientist

• Translate planning, operations and sales business questions into modelable problems with clear success metrics
• Build demand forecasting models, including for irregular and sparse demand
• Formulate and solve optimization problems with LP/MIP solvers
• Use simulation to test scenarios
• Deploy, version, monitor and retrain models in production in close collaboration with data engineering
• Build and maintain APIs and services using Python and FastAPI on AWS, Azure or GCP
• Follow security best practices
• Design, build and deploy AI-enhanced applications
• Take machine learning solutions through to production

Explore Data Scientist / ML Engineer at Ambush. Review requirements and application details.

Data Scientist / ML Engineer

• Build machine learning models and AI-powered solutions for business-critical use cases
• Manage the end-to-end modeling lifecycle, including data extraction, feature engineering, model evaluation, retraining, and real-time deployment
• Design and develop production-ready LLM applications, including RAG systems and multi-agent architectures
• Diagnose model performance issues and select appropriate approaches for each problem
• Lead initiatives from research and scope definition through delivery
• Collaborate with stakeholders and engineering teams across the US and India
• Lead projects or MVPs end to end, including requirements definition and stakeholder alignment
• Present technical work to US clients
• Build dashboards and communicate results to business audiences

Explore Data Scientist/AI Engineer at Ambush. Review requirements and application details.