Job Description

Job Title : Asc. - D&A Solutions Experience : 7-10 Years
Location : Mumbai
Key Responsibilities:
Demonstrate strong awareness of data, cloud, and AI-related risks, with the ability to interpret and apply governance policies and frameworks. Solid understanding of responsible AI practices and the evolving risk landscape of Generative AI. Design and implement automated control frameworks leveraging advanced analytics, AI/ML techniques, and risk-driven approaches to strengthen oversight across data, cloud, and AI platforms. Build, optimize, and maintain scalable and high-performing AI/ML models, ensuring accuracy, robustness, and alignment with business requirements. Define and implement best practices for model validation, testing, performance monitoring, and periodic upgrades, aligned with enterprise risk frameworks. Work closely with data engineers, software developers, and domain experts to ensure seamless integration and operationalization of AI/ML solutions within existing systems. Deliver end-to-end AI/ML-based solutions following secure and scalable SDLC standards, including data ingestion, security protocols, and enterprise architecture principles. Engage proactively with business stakeholders to identify AI opportunities, define solution requirements, and clearly communicate risk, control, and value implications related to data, cloud, and AI initiatives. Stay abreast of emerging trends in data science, ML, and AI, incorporating new technologies and approaches to enhance solution effectiveness and resilience.
Requisites:
Proven expertise in machine learning algorithms, deep learning architectures, and statistical modeling, with a focus on accuracy, scalability, and performance optimization. Hands-on experience in building and managing end-to-end ML pipelines, covering model training, testing, validation, and deployment. Proficient with cloud platforms such as AWS, GCP, and Azure, and experienced in using MLOps tools for automated model deployment, monitoring, and governance. Skilled in managing complex data ecosystems, including structured, semi-structured, and unstructured datasets, and solving ambiguous or undefined data problems. Capable of conducting AI model evaluations aligned with ethical AI frameworks; experienced in implementing controls to mitigate AI risks and ensure responsible usage. Strong communication and stakeholder engagement skills, enabling seamless collaboration with data scientists, engineers, product managers, and business teams. * Deep understanding of SDLC best practices, including data integration, secure application design, and scalable architecture.