Specialist Data Science

Nasdaq Technology is looking for a passionate Specialist - Data Science with a focus on software development, to join the Bangalore technology center in India. If Innovation and effectiveness drive you forward this is the place for you! Nasdaq Lens team in Bangalore. You will be responsible for building and delivering AI-powered solutions across the Nasdaq Lens program to help various business operations and process simplification, introduce automation via Artificial Intelligence, and drive efficiency and productivity. We are looking for candidates who share those values along with a genuine desire and drive to deliver top technology solutions to todays markets. With this position we offer: Join the development organization for the Nasdaq Lens COE team. The main objective for the development team in Bangalore is to build state of the art AI powered solutions to simplify operations process across CAP products and drive productivity. As our new member, you will work with a group of enthusiastic and experienced team members. Together with your team, you will be accountable for delivering enhancements and supporting the architecture of the enterprise product as it transitions to new software paradigms. Your role and responsibilities : We are seeking an experienced Specialist - Data Science to develop AI and machine learning projects from inception to deployment. The ideal candidate will bridge technical and business objectives while collaborating with cross-functional teams to deliver high-impact AI solutions. Design, build, and deploy Generative AI and Agentic AI solutions for complex business problems. Develop and orchestrate AI agents to automate operations and business processes. Build and integrate cloud-native, serverless AI workflows on AWS. Apply advanced prompting techniques and prompt optimization to improve model accuracy, reliability, and cost efficiency. Implement observability and monitoring for AI/agentic systems to ensure performance and reliability in production. Establish standards for model development, evaluation, validation, and deployment. Design and implement data pipelines and integrations with relational databases. Drive continuous improvement in solution accuracy, latency, and performance. Furthermore, you will: Collaborate with business stakeholders to identify opportunities for AI/ML solutions. Present technical findings and solution outcomes to stakeholders. Contribute to architectural decisions for AI systems. Ensure ethical and responsible AI practices. Lead POCs and research initiatives for emerging AI technologies. Partner with data engineering teams for efficient data pipeline development. We expect you to have: Minimum 5 to 6 years of experience in Data Science / Machine Learning / AI engineering. Must have core development experience on in Python and related AI/ML frameworks. Strong hands-on experience with AWS services: Lambda, Batch, Step Functions, Bedrock, Bedrock Agent Core, S3, RDS, and Redis (Elastic ache). Proven experience building AI Agents and agentic workflows. Experience with AI observability and monitoring for LLM and agentic applications. Hands-on experience with Anthropic Claude models via Amazon Bedrock. Expertise in prompting techniques and prompt optimization. Basic knowledge of PostgreSQL databases (queries, schema, integration) Proven track record of deploying AI/ML models and solutions to production. Excellence in technical communication and collaboration. Education Qualification: Bachelor / master's in computer science. It would be great if you: Have experience with MLOps and automated deployment pipelines. Possess knowledge of RAG architectures, vector databases, and NLP. Are experienced with evaluation frameworks for LLM and agentic systems. Have contributed to AI/ML open-source projects. Are familiar with AI governance and compliance frameworks. Disclaimer : This job posting has been aggregated from external source. Role detailscontentand availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.