Senior / Staff AI Scientist Ontology & Knowledge Engineering Asper.ai

8.0 - 12.0 years

0 Lacs

karnataka

On-site

You are a Senior or Staff AI Scientist responsible for leading the development of domain-specific business ontologies and knowledge engineering infrastructure. Your expertise will be utilized to leverage cutting-edge LLMs, GenAI, and symbolic reasoning to establish an ontology layer, which will serve as the cornerstone of the data platform. This initiative aims to facilitate intelligent data harmonization, enrichment, and decision automation at a large scale. Your key responsibilities will involve spearheading the research and development of ontology learning systems by leveraging state-of-the-art LLMs, structured data mining, and semantic reasoning techniques. You will be tasked with constructing and expanding domain-specific knowledge graphs that seamlessly integrate both external and internal data sources. Additionally, you will drive innovation in semi-automated ontology construction, concept disambiguation, and alignment using GenAI, contrastive learning, and knowledge distillation methodologies. Collaboration across departments is crucial in embedding ontology-driven intelligence into various pipelines, applications, and decision systems. You will work closely with data platform engineers, AI scientists, and product teams to ensure seamless integration and implementation of these intelligent systems. Furthermore, you will be responsible for defining core metrics to evaluate the quality of ontologies, knowledge coverage, and subsequent performance enhancements such as data harmonization and semantic search capabilities. Your role will also involve collaborating with ML infrastructure teams to optimize representation formats (RDF, Knowledge graphs, etc.) and enable scalable, low-latency retrieval and reasoning processes. To qualify for this position, you should hold a PhD or Masters degree in Computer Science, AI, Data Science, or a related field with a focus on knowledge representation, natural language understanding, or data systems. Moreover, you should possess a minimum of 8 years of experience in applied AI or ML research with a specific focus on ontologies, knowledge engineering, entity resolution, or semantic systems. Your expertise should extend to working with LLMs (e.g., GPT, LLaMA, PaLM, Claude) for knowledge extraction, generation, or few-shot learning. A strong background in data engineering, including schema mapping, metadata, master data management, or data pipelines at scale, is also essential. You are expected to have a deep understanding of semantic data models such as ontologies and hands-on experience with relevant libraries or frameworks. Proficiency in programming languages like Python, as well as frameworks like PyTorch or TensorFlow, and graph technologies such as Neo4j, RDFLib, SPARQL, etc., will be beneficial in fulfilling the requirements of this role.,

Posted 1 day ago

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Senior Lead Data Architect Chase- Candidate Experience page

5.0 - 9.0 years

0 Lacs

hyderabad, telangana

On-site

You strive to be an essential member of a diverse team of visionaries dedicated to making a lasting impact. Don't pass up this opportunity to collaborate with some of the brightest minds in the field and deliver best-in-class solutions to the industry. As a Senior Lead Data Architect at JPMorgan Chase within the Consumer and Community Banking Data Technology, you are an integral part of a team that works to develop high-quality data architecture solutions for various software applications, platform, and data products. Drive significant business impact and help shape the global target state architecture through your capabilities in multiple data architecture domains. Represents the data architecture team at technical governance bodies and provides feedback regarding proposed improvements regarding data architecture governance practices. Evaluates new and current technologies using existing data architecture standards and frameworks. Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors. Design secure, high-quality, scalable solutions and reviews architecture solutions designed by others. Drives data architecture decisions that impact data product & platform design, application functionality, and technical operations and processes. Serves as a function-wide subject matter expert in one or more areas of focus. Actively contributes to the data engineering community as an advocate of firmwide data frameworks, tools, and practices in the Software Development Life Cycle. Influences peers and project decision-makers to consider the use and application of leading-edge technologies. Advises junior architects and technologists. Required qualifications, capabilities, and skills: - Formal training or certification on software engineering concepts and 5+ years of applied experience. - Advanced knowledge of architecture, applications, and technical processes with considerable in-depth knowledge in data architecture discipline and solutions (e.g., data modeling, native cloud data services, business intelligence, artificial intelligence, machine learning, data domain driven design, etc.). - Practical cloud-based data architecture and deployment experience, preferably AWS. - Practical SQL development experiences in cloud-native relational databases, e.g. Snowflake, Athena, Postgres. - Ability to deliver various types of data models with multiple deployment targets, e.g. conceptual, logical, and physical data models deployed as operational vs. analytical data stores. - Advanced in one or more data engineering disciplines, e.g. streaming, ELT, event processing. - Ability to tackle design and functionality problems independently with little to no oversight. - Ability to evaluate current and emerging technologies to select or recommend the best solutions for the future state data architecture. Preferred qualifications, capabilities, and skills: - Financial services experience, card and banking a big plus. - Practical experience in modern data processing technologies, e.g., Kafka streaming, DBT, Spark, Airflow, etc. - Practical experience in data mesh and/or data lake. - Practical experience in machine learning/AI with Python development a big plus. - Practical experience in graph and semantic technologies, e.g. RDF, LPG, Neo4j, Gremlin. - Knowledge of architecture assessments frameworks, e.g. Architecture Trade-off Analysis.,

Posted 4 days ago

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