Job Description

Senior AI/ML Engineer

Job requirements

• 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.
• 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.
• 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.
• 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.