Software Engineer – Intelligent Automation

SOFTWARE ENGINEER - Intelligent Automation

Job Summary

The Software Engineer is responsible for designing, developing, testing, deploying, and supporting enterprise-grade intelligent automation and software solutions that improve business efficiency, productivity, and user experience. The role combines software engineering, automation, AI/GenAI integration, cloud technologies, DevOps practices, and production support responsibilities to deliver scalable and resilient digital solutions.

The successful candidate will work closely with business stakeholders, architects, product owners, and cross-functional technology teams to implement automation, AI-powered applications, APIs, integrations, and cloud-native solutions that align with organizational objectives.

Key Responsibilities

Software Development & Engineering

• Design, develop, and maintain scalable applications, automation solutions, APIs, and integrations.
• Write clean, maintainable, and reusable code following software engineering best practices.
• Develop backend services, microservices, and automation workflows.
• Participate in requirements analysis, solution design, code reviews, and technical documentation.
• Ensure application security, performance optimization, and adherence to engineering standards.

Programming & Scripting

• Develop solutions primarily using Python, Java, SQL, and other applicable programming languages.
• Create automation scripts, utilities, and reusable frameworks to improve operational efficiency.
• Build integrations using REST APIs, Graph APIs, web services, JSON, XML, and event-driven architectures.
• Troubleshoot and resolve application defects through debugging and root-cause analysis.

AI / Generative AI Development

• Integrate AI and Generative AI capabilities into enterprise applications and automation solutions.
• Develop prompt engineering strategies and orchestration patterns for GenAI use cases.
• Build AI-enabled workflows using Gemini, Claude, or other LLM platforms.
• Implement Retrieval-Augmented Generation (RAG), agentic workflows, MCP, tool/function calling, and AI orchestration frameworks.
• Evaluate, test, and optimize AI model outputs for security, quality, reliability, and business value.
• Support governance, responsible AI, and data privacy requirements.

Cloud Engineering

• Design and deploy solutions on cloud platforms such as Azure, GCP, or AWS.
• Develop and maintain cloud-native services, serverless functions, APIs, and containerized applications.
• Support cloud infrastructure, monitoring, performance optimization, and security controls.
• Implement scalable architectures that support high availability and business continuity.

DevOps & Platform Engineering

• Develop and maintain CI/CD pipelines using Azure DevOps, GitHub Actions or similar tools.
• Manage source control repositories and release management activities.
• Automate build, deployment, testing, and monitoring processes.
• Implement observability using logging, monitoring, alerting, and telemetry solutions.

Production Support & Operations

• Provide Level 2/Level 3 support for production applications, automation solutions, and AI services.
• Monitor solution health, job execution, system performance, and operational metrics.
• Perform incident management, problem management, and root cause analysis.
• Resolve production defects and service interruptions within established SLAs.
• Participate in on-call support and major incident recovery activities when required.
• Drive continuous improvement to enhance solution stability and reliability.

Agile / Scrum Delivery & Team Collaboration

• Actively participate in Scrum activities, including sprint planning, backlog refinement, daily stand-ups, sprint reviews, and retrospectives.
• Use Jira to manage user stories, technical tasks, defects, sprint commitments, and delivery progress.
• Collaborate with the Product Owner, Scrum Master, business stakeholders, architects, DevOps engineers, data engineers, and analysts.
• Provide accurate estimates, communicate delivery risks and dependencies, and contribute to Definition of Ready and Definition of Done discussions.
• Mentor junior engineers and contribute to technical knowledge sharing.

Required Qualifications

Education

• Bachelor's Degree in Computer Science, Information Technology, Computer Engineering, or related field.

Experience

• 5+ years of experience in software engineering, application development, or intelligent automation.
• Experience supporting production applications or enterprise automation platforms.
• Experience with Agile software development methodologies.

Technical Skills

Programming & Development: Python, Java, JavaScript / TypeScript, SQL, REST APIs, JSON / XML, Git Version Control

Intelligent Automation: Power Automate, Automation Anywhere, Workflow Automation, Process Orchestration, API Integration

AI / GenAI: Prompt Engineering, Azure OpenAI, Gemini, Claude, Agentic AI Frameworks, MCP, RAG Architectures, Tool/Function Calling

Cloud Technologies: Microsoft Azure, Google Cloud Platform (GCP), Cloud Functions, Azure Functions, Container Technologies

DevOps: Azure DevOps, GitHub, CI/CD Pipelines, Monitoring & Alerting, Release Management, Automated Testing

Production Support: Incident Management, Problem Management, Root Cause Analysis, Monitoring and Logging, Service Reliability, SLA Management

Soft Skills

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management skills.
• Ability to work independently and collaboratively in cross-functional teams.
• Strong debugging, troubleshooting, and decision-making capabilities.
• Continuous learning mindset with passion for AI and emerging technologies.

Success Measures

• Reliable and stable production systems with high availability.
• Successful delivery of automation and software development initiatives.
• Faster resolution of production incidents and reduced defect rates.
• Increased automation adoption and business value realization.
• Compliance with engineering, security, and governance standards.
• Contribution to AI/GenAI innovation and continuous improvement initiatives.