Senior AI/ML Engineer – R01571019

Brillio; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Brillio seeks an AI Production Engineer to turn agentic AI designs into secure, scalable production solutions. You will deploy agents, orchestration workflows, and integrations while ensuring robust security, testing, and observability.

The role emphasizes hands-on engineering for operationalizing AI, including tool calls, workflow orchestration, and enterprise integration patterns. Collaboration across architecture, security, and business teams will be essential.

Qualifications

• 4–5 years in software, cloud, integration, automation, or AI engineering.
• 1–2 years hands-on experience deploying LLM/agentic apps to production.
• Strong Python development and API/database knowledge.
• Experience with at least one agent framework or platform (e.g., LangGraph, AWS Bedrock, or similar).

Responsibilities

• Build production-grade AI agents and multi-step workflows based on approved architecture.
• Integrate agents with enterprise APIs, knowledge sources, and business apps.
• Implement tool calling, memory, human approvals, and recovery mechanisms.
• Develop reusable services and APIs for safe enterprise interactions.
• Apply responsible-AI and security controls including authentication and data protection.
• Establish automated testing for prompts, tools, integrations, and workflows.
• Set up monitoring for quality, latency, cost, and incidents.
• Build CI/CD pipelines and manage controlled releases.

Skills

Python

APIs

CI/CD

Cloud deployment

Agent frameworks

Tools

Git

Docker

Kubernetes

Terraform

Job description

AI Production Engineer – Agentic Systems Role Overview

We are looking for an AI Production Engineer to turn agentic AI designs into secure, reliable, and scalable production solutions. Working closely with Enterprise Architecture, Security, this role will build and deploy agents, orchestration workflows, integrations, and the supporting production control. This is a hands‑on engineering role focused on operationalizing AI, not AI research or model training. The successful candidate should be comfortable working with different agentic design patterns, technology platforms, and models.

Key Responsibilities

• Build and deploy production‑grade AI agents and multi‑step orchestration workflows based on approved architecture.
• Integrate agents with enterprise APIs, knowledge sources, business applications, databases, and automation platforms.
• Implement tool calling, workflow state, memory, human approvals, exception handling, and recovery mechanisms.
• Develop reusable services and APIs that allow agents to interact safely with enterprise systems.
• Apply responsible‑AI and security controls, including authentication, authorization, data protection, audit logging, and human oversight.
• Implement automated testing for prompts, tools, integrations, workflows, security controls, and end‑to‑end agent behaviour.
• Establish monitoring for quality, latency, cost, tool failures, model behaviour, and production incidents.
• Build CI/CD pipelines and support‑controlled releases, rollback, versioning, and environment management.
• Troubleshoot production issues and continuously improve agent reliability and performance.
• Partner with Architecture to translate approved patterns and standards into deployable solutions.

Required Experience

• 4–5 years of experience in software, cloud, integration, automation, or AI engineering.
• At least 1–2 years of hands‑on experience deploying LLM or agentic applications into production.
• Strong development experience in Python and working knowledge of APIs, event‑driven integrations, and databases.
• Experience with at least one agent framework or platform, Agents SDK, LangGraph, LangSmith, Semantic Kernel, AWS Bedrock, or a comparable solution.
• Experience implementing retrieval, tool calling, workflow orchestration, structured outputs, and human‑in‑the‑loop processes.
• Practical experience with Git, automated testing, CI/CD, containers, and cloud deployment.
• Experience with production monitoring, logging, alerting, incident investigation, and performance optimization.
• Understanding of enterprise security, identity, secrets management, access controls, and protection of sensitive data.
• Ability to work across architecture, security, platform, and business teams.

Preferred Experience

• Experience with Azure or AWS and infrastructure‑as‑code tools.
• Familiarity with Kubernetes, serverless services, API gateways, message queues, or workflow platforms.
• Experience evaluating agent quality, task completion, groundedness, tool selection, safety, latency, and cost.
• Understanding of tracing and observability across prompts, models, tools, APIs, and workflow steps.
• Experience integrating AI solutions with platforms such as SharePoint, Salesforce, ServiceNow, Jira, or enterprise data services.

What Success Looks Like

• Agentic solutions move from approved design to production through a repeatable and governed process.
• Deployments are secure, observable, testable, and recoverable.
• Agent decisions, tool calls, data access, failures, and human approvals are traceable.
• Solutions meet agreed expectations for reliability, quality, latency, cost, and responsible‑AI controls.