What are the responsibilities and job description for the AI Engineer position at Rivago infotech inc?
AI Engineer - AI Foundations and Platform Enablement
Location: Dallas, TX and Austin, TX
Location: Dallas, TX and Austin, TX
Role Summary
Design and build the foundational platform layers needed to deliver secure, scalable,
reusable AI use cases. The role will develop proofs of concept and production-ready
patterns across MCP, orchestration, security, caching, and telemetry.
Key Responsibilities
- Build POCs for MCP gateways and retail-domain MCP servers that securely expose
enterprise tools and data.
- Design an orchestration layer for coordinating models, agents, tools, workflows,
approvals, retries, and failures.
- Establish caching patterns that improve latency and cost while protecting data
freshness and privacy.
- Implement agentic authentication and authorization, including identity propagation,
delegated access, least privilege, and auditability.
- Create telemetry for AI workflows, including traces, metrics, logs, token usage, tool
calls, latency, errors, and policy decisions.
- Deliver reusable APIs, reference implementations, documentation, and standards for
application teams.
- Partner with architecture, security, product, and engineering teams to move POCs
toward production.
Must Have
- 5 years of software engineering experience building distributed services or platforms.
- Hands-on experience with LLM applications, AI agents, RAG, or tool-calling workflows.
- Strong programming skills in Java, Python, TypeScript, or Go.
- Experience with APIs, service integration, asynchronous processing, and distributed
systems.
- Practical knowledge of authentication, authorization, secrets management, and secure
service communication.
- Experience with observability, including structured logging, metrics, tracing, and
operational dashboards.
- Experience with cloud and containerized deployments, such as Kubernetes.
- Strong communication and collaboration skills, with the ability to turn ambiguous ideas
into working POCs.
Nice to Have
Design and build the foundational platform layers needed to deliver secure, scalable,
reusable AI use cases. The role will develop proofs of concept and production-ready
patterns across MCP, orchestration, security, caching, and telemetry.
Key Responsibilities
- Build POCs for MCP gateways and retail-domain MCP servers that securely expose
enterprise tools and data.
- Design an orchestration layer for coordinating models, agents, tools, workflows,
approvals, retries, and failures.
- Establish caching patterns that improve latency and cost while protecting data
freshness and privacy.
- Implement agentic authentication and authorization, including identity propagation,
delegated access, least privilege, and auditability.
- Create telemetry for AI workflows, including traces, metrics, logs, token usage, tool
calls, latency, errors, and policy decisions.
- Deliver reusable APIs, reference implementations, documentation, and standards for
application teams.
- Partner with architecture, security, product, and engineering teams to move POCs
toward production.
Must Have
- 5 years of software engineering experience building distributed services or platforms.
- Hands-on experience with LLM applications, AI agents, RAG, or tool-calling workflows.
- Strong programming skills in Java, Python, TypeScript, or Go.
- Experience with APIs, service integration, asynchronous processing, and distributed
systems.
- Practical knowledge of authentication, authorization, secrets management, and secure
service communication.
- Experience with observability, including structured logging, metrics, tracing, and
operational dashboards.
- Experience with cloud and containerized deployments, such as Kubernetes.
- Strong communication and collaboration skills, with the ability to turn ambiguous ideas
into working POCs.
Nice to Have
- Experience with Model Context Protocol, MCP gateways, MCP servers, or similar
agent integration frameworks.
- Experience building orchestration or workflow platforms with durable execution,
queues, event streams, or human-in-the-loop controls.
- Experience with Redis or other distributed caching technologies.
- Experience with Open Telemetry and AI observability or evaluation platforms.
- Knowledge of OAuth 2.0, OpenID Connect, workload identity, token exchange,
delegated authorization, or policy engines such as OPA.
- Experience in financial services, retail investing, brokerage, or another regulated
industry.
- Familiarity with responsible AI, data privacy, model governance, vector databases,
embeddings, and retrieval systems.
- Experience with CI/CD, infrastructure as code, automated testing, and performance
testing.
Expected Outcomes
- Working POCs for an MCP gateway, retail MCP server, and orchestration layer.
- Reusable patterns for secure agent access, caching, and AI telemetry.
- A practical roadmap for hardening foundational capabilities for production adoption.
agent integration frameworks.
- Experience building orchestration or workflow platforms with durable execution,
queues, event streams, or human-in-the-loop controls.
- Experience with Redis or other distributed caching technologies.
- Experience with Open Telemetry and AI observability or evaluation platforms.
- Knowledge of OAuth 2.0, OpenID Connect, workload identity, token exchange,
delegated authorization, or policy engines such as OPA.
- Experience in financial services, retail investing, brokerage, or another regulated
industry.
- Familiarity with responsible AI, data privacy, model governance, vector databases,
embeddings, and retrieval systems.
- Experience with CI/CD, infrastructure as code, automated testing, and performance
testing.
Expected Outcomes
- Working POCs for an MCP gateway, retail MCP server, and orchestration layer.
- Reusable patterns for secure agent access, caching, and AI telemetry.
- A practical roadmap for hardening foundational capabilities for production adoption.
Salary : $60 - $65