Principle AI/ML Engineer

Job Description Job Purpose - Lead the design, development, and implementation of Al/ML, Generative Al (Gen Al), and Agentic Al solutions aligned with business transformation objectives. - Create Business impact by streamlining the Al delivery pipeline and drive the seamless execution of Al related projects - Drive innovation by exploring new technology improvements in Al and conduct POCs for business cases by experimenting with existing data and infrastructures within Bank Develop scalable prototypes using Gen Al and Agentic Al frameworks. - Mentor the team members with Al expertise and guide them to upskill on Al related topics with both internal and external trainings - Research and publish Al advancement as part of market intelligence and involve in peer and vendor collaborations. - Collaborate with business stakeholders and technology teams to identify Al-driven opportunities that enhance operational efficiency, customer experience, and decision-making capabilities. - Responsible for end-to-end delivery of Al initiatives, including problem framing, data exploration, model development, validation, deployment, and post-production monitoring. - Provide functional and technical leadership across Al domainsensuring models are explainable, ethical, and in line with regulatory and governance requirements. - Work cross-functionally to integrate Al capabilities into existing platforms and workflows, while promoting the adoption of emerging technologies such as autonomous agents and intelligent decision systems. Key Result Areas - Act as a strategic AI/ML leader, identifying and executing use cases where Al can deliver automation, intelligent decision-making, predictive insights, and operational optimization. - Manage medium to complex Al/ML and Gen Al projects from concept to deployment, demonstrating strong independent contribution across solution design, development, and delivery. - Lead and coordinate cross-functional, geographically distributed teams (onshore, offshore, and outsourced) delivering critical Al applications and platforms enterprise-wide. - Translate business requirements into functional specifications, ensuring seamless alignment with Al/ML models, data pipelines, and intelligent automation strategies. - Conduct system and process analysis to identify opportunities for Al integration that enhance agility, customer experience, and internal efficiency. - Perform impact assessments for Al-driven system enhancements, evaluating potential disruptions and opportunities through intelligent simulations and scenario modeling. - Bridge the gap between domain experts and Al developers, translating complex business scenarios into structured data and model-ready formats. Independently prepare high-level scenarios and test data for Proof of Concept (POC) and internal testing of Al solutions without dependency on QA teams. - Lead root cause analysis (RCA) using Al-based diagnostics, anomaly detection tools, and log intelligence to prevent recurrence of system disruptions. - Maintain detailed documentation for Al system configurations, including model parameters, training datasets, pipeline dependencies, and operational workflows. - Apply awareness of API architecture, data lineage, and system access to ensure Al models are securely and efficiently integrated across platforms. - Use Al and ML tools to support debugging and resolution of complex issues, improving speed, accuracy, and reliability of technical troubleshooting. - Ensure AI/ML initiatives meet governance, audit, and regulatory compliance standards, with a focus on ethical Al, data privacy, and model explainability. - Lead employee engagement and capability-building activities, with emphasis on Al fluency, experimentation, and adoption across teams. - Oversee smooth transitions from development to production by embedding Al-based monitoring, alerting, and self-healing capabilities. - Rigorously plan, execute, and finalize Al initiatives within deadlines, using intelligent project management and predictive planning tools. - Coordinate with data engineering, infrastructure, and analytics teams to deliver seamless, scalable Al solutions that align with enterprise architecture. Maintain strict adherence to quality assurance and change management processes, exploring automation opportunities through Al-driven change risk assessments and documentation. - Contribute to the full lifecycle of Al and Gen Al systems-including ideation, model design, prompt engineering, testing, deployment, and iterative refinement. Perform model selection, evaluation, and fine-tuning for Gen Al and Agentic Al use cases; build and orchestrate autonomous agents for dynamic workflows and decision support. Knowledge, Skills And Experience - 18+ years of hands-on experience in Al/ML application development, with focus on Generative Al and a research-oriented approach. - Proven ability to apply theoretical knowledge to real-world challenges. - Proficient in Python programming. - Classification: Internal Use Expertise in ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-Learn, etc. - Expertise in both fundamental and advanced ML/GenAI techniques, including regression, classification, clustering, data synthesis, text/image processing, and more. Familiarity with message queues, Flask API/Fast API, storage and cloud technologies (Azure/AWS), Kubernetes/Docker, SQL, and testing frameworks. - Having hands-on experience with Azure Al services, including Azure Machine Learning, Cognitive Services, and Al-based solutions for business applications, will be considered as an asset. - Basic knowledge of Generative Al models, including Large Language Models (LLMs), Small Language Models (SLMs), and RetrievalAugmented Generation (RAG), OpenAl. - Familiarity with open-source models from Meta (Llama), Keras, Google, etc. Should have worked as part of functional consultant in Bank's Product Designing with respect to Business requirements Strong understanding of SDLC and testing process Strong inter-personal communication skills.