Your responsibilities include driving the adoption of automation platforms (such as RPA, IPA, and cloud-based tools), integrating Agentic AI for adaptive and context-aware workflow automation, and ensuring solutions are robust, compliant, and measurable. This is a unique opportunity to influence the company’s automation strategy and champion the next wave of AI-enabled enterprise productivity. 

1. AI Application Development & Delivery 

• Lead the design, development, and deployment of AI/ML applications that solve high-impact business problems. 

• Translate business requirements into technical solutions leveraging advanced AI/ML techniques. 

• Ensure AI models are integrated into user-friendly applications with robust APIs, dashboards, and enterprise systems. 

• Drive best practices in software engineering, MLOps, and scalable cloud deployment. 

2. Team Leadership & Mentoring 

• Manage a team of AI engineers and developers, setting goals, guiding technical execution, and fostering growth. 

• Mentor junior team members, promoting excellence in AI coding practices, model deployment, and system design. 

• Ensure the team delivers high-quality, production-ready AI applications on time. 

3. Architecture & Technology Strategy 

• Define architecture standards for AI application development and integration with enterprise platforms. 

• Ensure applications are scalable, secure, and compliant with industry regulations. 

• Stay ahead of emerging AI technologies (GenAI, Agentic AI, LLMOps) and assess their applicability for enterprise adoption. 

4. Stakeholder Engagement & Collaboration 

• Partner with product managers, data scientists, business teams, and senior leaders to align solutions with strategic priorities. 

• Clearly communicate complex AI concepts and application value to both technical and non-technical stakeholders. 

• Build strong relationships with vendors, cloud providers, and external partners to accelerate innovation. 

5. Governance, Risk & Continuous Improvement 

• Implement best practices for governance, risk management, and compliance in AI application delivery. 

• Monitor and optimize application performance to ensure scalability and reliability. 

• Continuously enhance development practices to improve speed, quality, and maintainability of AI solutions. 

Qualifications & Experiences: 

Academic Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field. 

• Certifications in AI/ML, Cloud (AWS/Azure/GCP), or application development frameworks are desirable. 

Professional Experience: 

• 8–10 years of professional experience in AI/ML, software engineering, or application development. 

• Proven track record of building and deploying AI-powered applications at scale in enterprise environments. 

• Strong experience with Python, Java/JavaScript, or similar programming languages for AI integration. 

• Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and cloud AI platforms (AWS SageMaker, Azure AI, Bedrock, etc.). 

• Exposure to GenAI/LLM-based applications, prompt engineering, and API-based deployments is highly desirable. 

• At least 3+ years of leadership/managerial experience managing teams of developers/engineers. 

Technical Skills: 

• Strong proficiency in Python and ML pipeline development. 

• Experience with MLOps tools (MLflow, Kubeflow, Airflow) and CI/CD for AI. 

• Familiarity with microser Official notification