Chief manager - application

Job description

The AI Technical Lead will be the technical backbone of the AI CoE, responsible for designing scalable AI architectures, leading AI engineering teams, and delivering enterprise-grade AI solutions. The role requires expertise in GenAI, LLMs, RAG, Machine Learning, API integration, cloud platforms, and enterprise application architecture. The ideal candidate is a hands-on technology leader who can design complex AI solutions while mentoring engineers and collaborating closely with business, product, and enterprise architecture teams. # Key Responsibilities ## AI Solution Architecture * Design scalable, secure, and production-ready AI solution architectures. * Define enterprise AI reference architecture and engineering standards. * Build reusable AI frameworks, accelerators, and shared services. * Evaluate and recommend appropriate AI models, frameworks, and technologies based on business requirements. * Ensure AI solutions align with enterprise architecture, cybersecurity, and governance standards. * Define integration patterns with enterprise applications, APIs, and cloud services. ## Generative AI \& LLM Engineering Lead the design and implementation of enterprise GenAI solutions including: * Enterprise AI Copilots * Customer Service AI Assistants * Sales \& Relationship Manager Assistants * Loan Underwriting Assistants * AI Knowledge Assistants * Document Intelligence Solutions * AI Search Platforms * Conversational AI * Voice AI * Multi-Agent AI Systems * Autonomous Workflow Agents **Develop Enterprise Solutions Using** * Retrieval-Augmented Generation (RAG) * Prompt Engineering * Context Management * Agent Orchestration * Semantic Search * Vector Databases * Function Calling * AI Workflow Automation ## AI Engineering Leadership * Lead and mentor a team of GenAI Engineers, ML Engineers, and AI Developers. * Conduct technical design reviews and code reviews. * Establish AI engineering standards and development best practices. * Drive innovation and continuous improvement. * Ensure high-quality, secure, and maintainable code. * Resolve technical challenges and guide the team on architecture decisions. ## Machine Learning Engineering **Guide The Development And Deployment Of** * Predictive Models * Classification Models * Recommendation Engines * Customer Segmentation Models * Fraud Detection Models * Credit Scoring Models * Risk Prediction Models * NLP Applications * Time-Series Forecasting Models Support the team in model optimization, validation, deployment, and monitoring. ## Enterprise AI Platform Development **Work With AI Platform And MLOps Teams To** * Build scalable AI platforms. * Develop reusable APIs and AI microservices. * Implement model deployment pipelines. * Manage model versioning and lifecycle. * Establish model monitoring and observability. * Optimize infrastructure utilization and cost. ## Enterprise Integration **Lead AI Integration With Enterprise Platforms Including** * CRM * Loan Origination Systems (LOS) * Loan Management Systems (LMS) * Mobile Applications * Data Lake * Customer Portals * Contact Centre Platforms * Marketing Automation Platforms * API Gateway * Enterprise Service Bus (ESB) * Payment Systems * FinTech Integrations ## Cloud \& DevOps * Build cloud-native AI solutions. * Design scalable deployment architectures. * Optimize AI infrastructure for performance and cost. * Implement CI/CD pipelines for AI applications. * Collaborate with DevOps teams for deployment automation. * Ensure high availability and disaster recovery readiness. ## Security \& Responsible AI * Implement secure AI development practices. * Ensure compliance with enterprise security policies. * Build explainable and transparent AI solutions. * Work closely with AI Governance teams to ensure Responsible AI practices. * Support security audits and regulatory compliance requirements. ## Stakeholder Collaboration **Work Closely With** * AI Product Manager * AI Solution Architect * Head - AI \& Intelligent Automation * Business Heads * Enterprise Architecture Team * Information Security * Infrastructure Teams * Data Engineering Teams * Compliance * Risk * Operations * External Technology Partners Translate business requirements into scalable technical solutions. # Educational Qualifications * Bachelor's Degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related field. ### Preferred * Master's Degree in Computer Science, Artificial Intelligence, Machine Learning, or Data Science. ### Certifications (Preferred) * Microsoft Azure AI Engineer Associate * AWS Certified Machine Learning - Specialty * Google Professional Machine Learning Engineer * Databricks Machine Learning Professional * Kubernetes Certification * TOGAF Certification * Azure Solutions Architect Expert # Experience * 10-14 years of overall software engineering experience. * Minimum 5 years of experience designing AI/ML solutions. * Minimum 3 years of experience leading AI engineering teams. * Hands-on experience building and deploying production-grade GenAI applications. * Experience in Banking, NBFC, Insurance, Financial Services, or FinTech preferred.