111 Sparql Jobs - Page 5

Sr Semantic Engineer – Research Data and Analytics What you will do Let’s do this. Let’s change the world. In this vital role you will be part of Research’s Semantic Graph Team is seeking a dedicated and skilled Semantic Data Engineer to build and optimize knowledge graph-based software and data resources. This role primarily focuses on working with technologies such as RDF, SPARQL, and Python. In addition, the position involves semantic data integration and cloud-based data engineering. The ideal candidate should possess experience in the pharmaceutical or biotech industry, demonstrate deep technical skills, and be proficient with big data technologies and demonstrate experience in semantic modeling. A deep understanding of data architecture and ETL processes is also essential for this role. In this role, you will be responsible for constructing semantic data pipelines, integrating both relational and graph-based data sources, ensuring seamless data interoperability, and leveraging cloud platforms to scale data solutions effectively. Roles & Responsibilities: Develop and maintain semantic data pipelines using Python, RDF, SPARQL, and linked data technologies. Develop and maintain semantic data models for biopharma scientific data Integrate relational databases (SQL, PostgreSQL, MySQL, Oracle, etc.) with semantic frameworks. Ensure interoperability across federated data sources, linking relational and graph-based data. Implement and optimize CI/CD pipelines using GitLab and AWS. Leverage cloud services (AWS Lambda, S3, Databricks, etc.) to support scalable knowledge graph solutions. Collaborate with global multi-functional teams, including research scientists, Data Architects, Business SMEs, Software Engineers, and Data Scientists to understand data requirements, design solutions, and develop end-to-end data pipelines to meet fast-paced business needs across geographic regions. Collaborate with data scientists, engineers, and domain experts to improve research data accessibility. Adhere to standard processes for coding, testing, and designing reusable code/components. Explore new tools and technologies to improve ETL platform performance. Participate in sprint planning meetings and provide estimations on technical implementation. Maintain comprehensive documentation of processes, systems, and solutions. Harmonize research data to appropriate taxonomies, ontologies, and controlled vocabularies for context and reference knowledge. What we expect of you We are all different, yet we all use our unique contributions to serve patients. Basic Qualifications and Experience: Doctorate Degree OR Master’s degree with 4 - 6 years of experience in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field OR Bachelor’s degree with 6 - 8 years of experience in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field OR Diploma with 10 - 12 years of experience in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field Preferred Qualifications and Experience: 6+ years of experience in designing and supporting biopharma scientific research data analytics (software platforms) Functional Skills: Must-Have Skills: Advanced Semantic and Relational Data Skills: Proficiency in Python, RDF, SPARQL, Graph Databases (e.g. Allegrograph), SQL, relational databases, ETL pipelines, big data technologies (e.g. Databricks), semantic data standards (OWL, W3C, FAIR principles), ontology development and semantic modeling practices. Cloud and Automation ExpertiseGood experience in using cloud platforms (preferably AWS) for data engineering, along with Python for automation, data federation techniques, and model-driven architecture for scalable solutions. Technical Problem-SolvingExcellent problem-solving skills with hands-on experience in test automation frameworks (pytest), scripting tasks, and handling large, complex datasets. Good-to-Have Skills: Experience in biotech/drug discovery data engineering Experience applying knowledge graphs, taxonomy and ontology concepts in life sciences and chemistry domains Experience with graph databases (Allegrograph, Neo4j, GraphDB, Amazon Neptune) Familiarity with Cypher, GraphQL, or other graph query languages Experience with big data tools (e.g. Databricks) Experience in biomedical or life sciences research data management Soft Skills: Excellent critical-thinking and problem-solving skills Good communication and collaboration skills Demonstrated awareness of how to function in a team setting Demonstrated presentation skills What you can expect of us As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way. In addition to the base salary, Amgen offers competitive and comprehensive Total Rewards Plans that are aligned with local industry standards. Apply now and make a lasting impact with the Amgen team. careers.amgen.com As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease. Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Associate Data Engineer Graph – Research Data and Analytics What you will do Let’s do this. Let’s change the world. In this vital role you will be part of Research’s Semantic Graph. Team is seeking a dedicated and skilled Data Engineer to design, build and maintain solutions for scientific data that drive business decisions for Research. You will build scalable and high-performance, graph-based, data engineering solutions for large scientific datasets and collaborate with Research partners. The ideal candidate possesses experience in the pharmaceutical or biotech industry, demonstrates deep technical skills, has experience with semantic data modeling and graph databases, and understands data architecture and ETL processes. Roles & Responsibilities: Design, develop, and implement data pipelines, ETL/ELT processes, and data integration solutions Contribute to data pipeline projects from inception to deployment, manage scope, timelines, and risks Contribute to data models for biopharma scientific data, data dictionaries, and other documentation to ensure data accuracy and consistency Optimize large datasets for query performance Collaborate with global multi-functional teams including research scientists to understand data requirements and design solutions that meet business needs Implement data security and privacy measures to protect sensitive data Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions Collaborate with Data Architects, Business SMEs, Software Engineers and Data Scientists to design and develop end-to-end data pipelines to meet fast paced business needs across geographic regions Identify and resolve data-related challenges Adhere to best practices for coding, testing, and designing reusable code/component Explore new tools and technologies that will help to improve ETL platform performance Participate in sprint planning meetings and provide estimations on technical implementation Maintain documentation of processes, systems, and solutions What we expect of you We are all different, yet we all use our unique contributions to serve patients. Basic Qualifications and Experience: Bachelor’s degree and 1to 3 years of Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field experience OR Diploma and 4 to 7 years of Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field experience Functional Skills: Must-Have Skills: Advanced Semantic and Relational Data Skills: Proficiency in Python, RDF, SPARQL, Graph Databases (e.g. Allegrograph), SQL, relational databases, ETL pipelines, big data technologies (e.g. Databricks), semantic data standards (OWL, W3C, FAIR principles), ontology development and semantic modeling practices. Hands on experience with big data technologies and platforms, such as Databricks, workflow orchestration, performance tuning on data processing. Excellent problem-solving skills and the ability to work with large, complex datasets Good-to-Have Skills: A passion for tackling complex challenges in drug discovery with technology and data Experience with system administration skills, such as managing Linux and Windows servers, configuring network infrastructure, and automating tasks with shell scripting. Examples include setting up and maintaining virtual machines, troubleshooting server issues, and ensuring data security through regular updates and backups. Solid understanding of data modeling, data warehousing, and data integration concepts Solid experience using RDBMS (e.g. Oracle, MySQL, SQL server, PostgreSQL) Knowledge of cloud data platforms (AWS preferred) Experience with data visualization tools (e.g. Dash, Plotly, Spotfire) Experience with diagramming and collaboration tools such as Miro, Lucidchart or similar tools for process mapping and brainstorming Experience writing and maintaining user documentation in Confluence Professional Certifications: Databricks Certified Data Engineer Professional preferred Soft Skills: Excellent critical-thinking and problem-solving skills Good communication and collaboration skills Demonstrated awareness of how to function in a team setting Demonstrated presentation skills What you can expect of us As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way. In addition to the base salary, Amgen offers competitive and comprehensive Total Rewards Plans that are aligned with local industry standards. Apply now and make a lasting impact with the Amgen team. careers.amgen.com As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease. Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

ABOUT AMGEN Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today. ABOUT THE ROLE Role Description We are seeking a Reference Data Management Senior Analyst who a s the Reference Data Product team member of the Enterprise Data Management organization, will be responsible for managing and promoting the use of reference data, partnering with business Subject Mater Experts on creation of vocabularies / taxonomies and ontologies, and developing analytic solutions using semantic technologies . Roles & Responsibilities Work with Reference Data Product Owner, external resources and other engineers as part of the product team? ? Develop and maintain semantically appropriate concepts ? ? Identify and address conceptual gaps in both content and taxonomy ? Maintain ontology source vocabularies for new or edited codes? ? Support product teams to help them leverage taxonomic solutions ? Analyze the data from public/internal datasets. ? Develop a Data Model/schema for taxonomy. ? Create a taxonomy in Semaphore Ontology Editor. ? Perform Bulk-import data templates into Semaphore to add/update terms in taxonomies. ? Prepare SPARQL queries to generate adhoc reports. ? Perform Gap Analysis on current and updated data? ? Maintain taxonomies in Semaphore through Change Management process. ? Develop and optimize automated data ingestion / pipelines through Python/ PySpark when APIs are available ? Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs ? Identify and resolve complex data-related challenges ? Participate in sprint planning meetings and provide estimations on technical implementation . Basic Qualifications and Experience Master’s degree with 6 years of experience in Business, Engineering, IT or related field OR Bachelor’s degree with 8 years of experience in Business, Engineering, IT or related field OR Diploma with 9+ years of experience in Business, Engineering, IT or related field Functional Skills: Must-Have Skills: Knowledge of controlled vocabularies, classification, ontology and taxonomy? ? Experience in ontology development using Semaphore, or a similar tool? ? Hands on experience writing SPARQL queries on graph data ? Excellent problem-solving skills and the ability to work with large, complex datasets ? Understanding of data modeling, data warehousing, and data integration concepts Good-to-Have Skills: Hands on experience writing SQL using any RDBMS (Redshift, Postgres, MySQL, Teradata, Oracle, etc.). ? Experience using cloud services such as AWS or Azure or GCP? ? Experience working in Product Teams environment ? Knowledge of Python/R, Databricks, cloud data platforms ? Knowledge of NLP (Natural Language Processing) and AI (Artificial Intelligence) for extracting and standardizing controlled vocabularies. ? Strong understanding of data governance frameworks, tools, and best practices ? Professional Certifications Databricks Certificate preferred? SAFe ® Practitioner Certificate preferred? Any Data Analysis certification (SQL , Python ) Any cloud certification (AWS or AZURE) Soft Skills: Strong analytical abilities to assess and improve master data processes and solutions. Excellent verbal and written communication skills, with the ability to convey complex data concepts clearly to technical and non-technical stakeholders. Effective problem-solving skills to address data-related issues and implement scalable solutions. Ability to work effectively with global, virtual teams EQUAL OPPORTUNITY STATEMENT Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status. We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Project Role : Application Architect Project Role Description : Provide functional and/or technical expertise to plan, analyze, define and support the delivery of future functional and technical capabilities for an application or group of applications. Assist in facilitating impact assessment efforts and in producing and reviewing estimates for client work requests. Must have skills : Manufacturing Operations Good to have skills : NA Minimum 12 year(s) of experience is required Educational Qualification : BTech BE Job Title:Industrial Data Architect Summary :We are seeking a highly skilled and experienced Industrial Data Architect with a proven track record in providing functional and/or technical expertise to plan, analyse, define and support the delivery of future functional and technical capabilities for an application or group of applications. Well versed with OT data quality, Data modelling, data governance, data contextualization, database design, and data warehousing. Must have Skills:Domain knowledge in areas of Manufacturing IT OT in one or more of the following verticals Automotive, Discrete Manufacturing, Consumer Packaged Goods, Life ScienceKey Responsibilities: Industrial Data Architect will be responsible for developing and overseeing the industrial data architecture strategies to support advanced data analytics, business intelligence, and machine learning initiatives. This role involves collaborating with various teams to design and implement efficient, scalable, and secure data solutions for industrial operations. Focused on designing, building, and managing the data architecture of industrial systems. Assist in facilitating impact assessment efforts and in producing and reviewing estimates for client work requests. Own the offerings and assets on key components of data supply chain, data governance, curation, data quality and master data management, data integration, data replication, data virtualization. Create scalable and secure data structures, integrating with existing systems and ensuring efficient data flow. Qualifications: Data Modeling and Architecture:oProficiency in data modeling techniques (conceptual, logical, and physical models).oKnowledge of database design principles and normalization.oExperience with data architecture frameworks and methodologies (e.g., TOGAF). Database Technologies:oRelational Databases:Expertise in SQL databases such as MySQL, PostgreSQL, Oracle, and Microsoft SQL Server.oNoSQL Databases:Experience with at least one of the NoSQL databases like MongoDB, Cassandra, and Couchbase for handling unstructured data.oGraph Databases:Proficiency with at least one of the graph databases such as Neo4j, Amazon Neptune, or ArangoDB. Understanding of graph data models, including property graphs and RDF (Resource Description Framework).oQuery Languages:Experience with at least one of the query languages like Cypher (Neo4j), SPARQL (RDF), or Gremlin (Apache TinkerPop). Familiarity with ontologies, RDF Schema, and OWL (Web Ontology Language). Exposure to semantic web technologies and standards. Data Integration and ETL (Extract, Transform, Load):oProficiency in ETL tools and processes (e.g., Talend, Informatica, Apache NiFi).oExperience with data integration tools and techniques to consolidate data from various sources. IoT and Industrial Data Systems:oFamiliarity with Industrial Internet of Things (IIoT) platforms and protocols (e.g., MQTT, OPC UA).oExperience with either of IoT data platforms like AWS IoT, Azure IoT Hub, and Google Cloud IoT Core.oExperience working with one or more of Streaming data platforms like Apache Kafka, Amazon Kinesis, Apache FlinkoAbility to design and implement real-time data pipelines. Familiarity with processing frameworks such as Apache Storm, Spark Streaming, or Google Cloud Dataflow.oUnderstanding of event-driven design patterns and practices. Experience with message brokers like RabbitMQ or ActiveMQ.oExposure to the edge computing platforms like AWS IoT Greengrass or Azure IoT Edge AI/ML, GenAI:oExperience working on data readiness for feeding into AI/ML/GenAI applicationsoExposure to machine learning frameworks such as TensorFlow, PyTorch, or Keras. Cloud Platforms:oExperience with cloud data services from at least one of the providers like AWS (Amazon Redshift, AWS Glue), Microsoft Azure (Azure SQL Database, Azure Data Factory), and Google Cloud Platform (BigQuery, Dataflow). Data Warehousing and BI Tools:oExpertise in data warehousing solutions (e.g., Snowflake, Amazon Redshift, Google BigQuery).oProficiency with Business Intelligence (BI) tools such as Tableau, Power BI, and QlikView. Data Governance and Security:oUnderstanding of data governance principles, data quality management, and metadata management.oKnowledge of data security best practices, compliance standards (e.g., GDPR, HIPAA), and data masking techniques. Big Data Technologies:oExperience in big data platforms and tools such as Hadoop, Spark, and Apache Kafka.oUnderstanding of distributed computing and data processing frameworks. Excellent Communication:Superior written and verbal communication skills, with the ability to effectively articulate complex technical concepts to diverse audiences. Problem-Solving Acumen:A passion for tackling intricate challenges and devising elegant solutions. Collaborative Spirit:A track record of successful collaboration with cross-functional teams and stakeholders. Certifications:AWS Certified Data Engineer Associate / Microsoft Certified:Azure Data Engineer Associate / Google Cloud Certified Professional Data Engineer certification is mandatory Minimum of 14-18 years progressive information technology experience. Qualifications BTech BE

We are seeking a seasoned Data Science Engineer to spearhead the development of intelligent, autonomous AI systems. The ideal candidate will have a robust background in agentic AI, LLMs, SLMs, vector DB, and knowledge graphs. This role involves designing and deploying AI solutions that leverage Retrieval-Augmented Generation (RAG), multi-agent frameworks, and hybrid search techniques to enhance enterprise applications. Your key responsibilities Design & Develop Agentic AI Applications:Utilise frameworks like LangChain, CrewAI, and AutoGen to build autonomous agents capable of complex task execution. Implement RAG Pipelines: Integrate LLMs with vector databases (e.g., Milvus, FAISS) and knowledge graphs (e.g., Neo4j) to create dynamic, context-aware retrieval systems. Fine-Tune Language Models:Customise LLMs (e.g., Gemini, chatgpt, Llama) and SLMs (e.g., Spacy, NLTK) using domain-specific data to improve performance and relevance in specialised applications. NER Models: Train OCR and NLP leveraged models to parse domain-specific details from documents (e.g., DocAI, Azure AI DIS, AWS IDP) Develop Knowledge Graphs: Construct and manage knowledge graphs to represent and query complex relationships within data, enhancing AI interpretability and reasoning. Collaborate Cross-Functionally:Work with data engineers, ML researchers, and product teams to align AI solutions with business objectives and technical requirements. Optimise AI Workflows:Employ MLOps practices to ensure scalable, maintainable, and efficient AI model deployment and monitoring. Your skills and experience 8+ years of professional experience in AI/ML development, with a focus on agentic AI systems. Proficient in Python, Python API frameworks, SQL and familiar with AI/ML frameworks such as TensorFlow or PyTorch. Experience in deploying AI models on cloud platforms (e.g., GCP, AWS). Experience with LLMs (e.g., GPT-4), SLMs (Spacy), and prompt engineering. Understanding of semantic technologies, ontologies, and RDF/SPARQL. Familiarity with MLOps tools and practices for continuous integration and deployment. Skilled in building and querying knowledge graphs using tools like Neo4j. Hands-on experience with vector databases and embedding techniques. Familiarity with RAG architectures and hybrid search methodologies. Experience in developing AI solutions for specific industries such as healthcare, finance, or e-commerce. Strong problem-solving abilities and analytical thinking. Excellent communication skills for cross-functional collaboration. Ability to work independently and manage multiple projects simultaneously.

The Role : The Knowledge Engineering team are seeking a Lead Knowledge Engineer to support our strategic transformation from a traditional data organization into a next generation interconnected data intelligence organization. The Team : The Knowledge Engineering team within data strategy and governance helps to lead fundamental organizational and operational change driving our linked data, open data, and data governance strategy, both internally and externally. The team partners closely with data and software engineering to envision and build the next generation of data architecture and tooling with modern technologies. The Impact : Knowledge Engineering efforts occur within the broader context of major strategic initiatives to extend market leadership and build next-generation data, insights and analytics products that are powered by our world class datasets. Whats in it for you : The Lead Knowledge Engineer role is an opportunity to work as an individual contributor in creatively solving complex challenges alongside visionary leadership and colleagues. Its a role with highly visible initiatives and outsized impact. The wider division has a great culture of innovation, collaboration, and flexibility with a focus on delivery. Every person is respected and encouraged to be their authentic self. Responsibilities : Develop, implement, and continue to enhance ontologies, taxonomies, knowledge graphs, and related semantic artefacts for interconnected data, as well as topical/indexed query, search, and asset discovery Design and prototype data software engineering solutions enabling to scale the construction, maintenance and consumption of semantic artefacts and interconnected data layer for various application contexts Provide thought leadership for strategic projects ensuring timelines are feasible, work is effectively prioritized, and deliverables met Influence the strategic semantic vision, roadmap, and next-generation architecture Execute on the interconnected data vision by creating linked metadata schemes to harmonize semantics across systems and domains Analyze and implement knowledge organization strategies using tools capable of metadata management, ontology management, and semantic enrichment Influence and participate in governance bodies to advocate for the use of established semantics and knowledge-based tools Qualifications: Able to communicate complex technical strategies and concepts in a relatable way to both technical and non-technical stakeholders and executives to effectively persuade and influence 5+ years of experience with ontology development, semantic web technologies (RDF, RDFS, OWL, SPARQL) and open-source or commercial semantic tools (e.g., VocBench, TopQuadrant, PoolParty, RDFLib, triple stores); Advanced studies in computer science, knowledge engineering, information sciences, or related discipline preferred 3+ years of experience in advanced data integration with semantic and knowledge graph technologies in complex, enterprise-class, multi-system environment(s); skilled in all phases from conceptualization to optimization Programming skills in a mainstream programming language (Python, Java, JavaScript), with experience in utilizing cloud services (AWS, Google Cloud, Azure) is a great bonus Understanding of the agile development life cycle and the broader data management discipline (data governance, data quality, metadata management, reference and master data management)

The Role : The Knowledge Engineering team are seeking a Lead Knowledge Engineer to support our strategic transformation from a traditional data organization into a next generation interconnected data intelligence organization. The Team : The Knowledge Engineering team within data strategy and governance helps to lead fundamental organizational and operational change driving our linked data, open data, and data governance strategy, both internally and externally. The team partners closely with data and software engineering to envision and build the next generation of data architecture and tooling with modern technologies. The Impact : Knowledge Engineering efforts occur within the broader context of major strategic initiatives to extend market leadership and build next-generation data, insights and analytics products that are powered by our world class datasets. Whats in it for you : The Lead Knowledge Engineer role is an opportunity to work as an individual contributor in creatively solving complex challenges alongside visionary leadership and colleagues. Its a role with highly visible initiatives and outsized impact. The wider division has a great culture of innovation, collaboration, and flexibility with a focus on delivery. Every person is respected and encouraged to be their authentic self. Responsibilities : Develop, implement, and continue to enhance ontologies, taxonomies, knowledge graphs, and related semantic artefacts for interconnected data, as well as topical/indexed query, search, and asset discovery Design and prototype data software engineering solutions enabling to scale the construction, maintenance and consumption of semantic artefacts and interconnected data layer for various application contexts Provide thought leadership for strategic projects ensuring timelines are feasible, work is effectively prioritized, and deliverables met Influence the strategic semantic vision, roadmap, and next-generation architecture Execute on the interconnected data vision by creating linked metadata schemes to harmonize semantics across systems and domains Analyze and implement knowledge organization strategies using tools capable of metadata management, ontology management, and semantic enrichment Influence and participate in governance bodies to advocate for the use of established semantics and knowledge-based tools Qualifications: Able to communicate complex technical strategies and concepts in a relatable way to both technical and non-technical stakeholders and executives to effectively persuade and influence 5+ years of experience with ontology development, semantic web technologies (RDF, RDFS, OWL, SPARQL) and open-source or commercial semantic tools (e.g., VocBench, TopQuadrant, PoolParty, RDFLib, triple stores); Advanced studies in computer science, knowledge engineering, information sciences, or related discipline preferred 3+ years of experience in advanced data integration with semantic and knowledge graph technologies in complex, enterprise-class, multi-system environment(s); skilled in all phases from conceptualization to optimization Programming skills in a mainstream programming language (Python, Java, JavaScript), with experience in utilizing cloud services (AWS, Google Cloud, Azure) is a great bonus Understanding of the agile development life cycle and the broader data management discipline (data governance, data quality, metadata management, reference and master data management)

Bachelor's degree in Computer Science, Software Engineering, or a related field. 5+ years of experience in software development, with a focus on large-scale data systems, knowledge graphs, or graph-based technologies. Proficiency in programming languages such as Python, Java, or Scala for data processing and production code development. Strong understanding of graph data structures, algorithms, and graph database technologies (e.g., Neo4j, RDF-tiple, JanusGraph, Amazon Neptune). Experience with database systems (SQL and NoSQL), data modeling techniques, and graph query languages such as Cypher or SPARQL. Knowledge of API design and microservices architecture for exposing knowledge graph data. Experience with big data technologies like Spark and Hadoop for data ingestion and processing. Strong problem-solving skills and the ability to translate complex business needs into technical solutions. Excellent communication and collaboration skills to work effectively with cross-functional teams. Experience with graph analytics and graph algorithms (e.g., centrality, community detection, link prediction) is highly beneficial.

: Job TitleSenior Data Science Engineer Lead LocationBangalore, India Role Description We are seeking a seasoned Data Science Engineer to spearhead the development of intelligent, autonomous AI systems. The ideal candidate will have a robust background in agentic AI, LLMs, SLMs, Vector DB, and knowledge graphs. This role involves designing and deploying AI solutions that leverage Retrieval-Augmented Generation (RAG), multi-agent frameworks, and hybrid search techniques to enhance enterprise applications. Deutsche Banks Corporate Bank division is a leading provider of cash management, trade finance and securities finance. We complete green-field projects that deliver the best Corporate Bank - Securities Services products in the world. Our team is diverse, international, and driven by shared focus on clean code and valued delivery. At every level, agile minds are rewarded with competitive pay, support, and opportunities to excel. You will work as part of a cross-functional agile delivery team. You will bring an innovative approach to software development, focusing on using the latest technologies and practices, as part of a relentless focus on business value. You will be someone who sees engineering as team activity, with a predisposition to open code, open discussion and creating a supportive, collaborative environment. You will be ready to contribute to all stages of software delivery, from initial analysis right through to production support. What we'll offer you As part of our flexible scheme, here are just some of the benefits that youll enjoy Best in class leave policy Gender neutral parental leaves 100% reimbursement under childcare assistance benefit (gender neutral) Sponsorship for Industry relevant certifications and education Employee Assistance Program for you and your family members Comprehensive Hospitalization Insurance for you and your dependents Accident and Term life Insurance Complementary Health screening for 35 yrs. and above Your key responsibilities Design & Develop Agentic AI ApplicationsUtilize frameworks like LangChain, CrewAI, and AutoGen to build autonomous agents capable of complex task execution. Implement RAG PipelinesIntegrate LLMs with vector databases (e.g., Milvus, FAISS) and knowledge graphs (e.g., Neo4j) to create dynamic, context-aware retrieval systems. Fine-Tune Language ModelsCustomize LLMs and SLMs using domain-specific data to improve performance and relevance in specialized applications. NER ModelsTrain OCR and NLP leveraged models to parse domain-specific details from documents (e.g., DocAI, Azure AI DIS, AWS IDP) Develop Knowledge GraphsConstruct and manage knowledge graphs to represent and query complex relationships within data, enhancing AI interpretability and reasoning. Collaborate Cross-FunctionallyWork with data engineers, ML researchers, and product teams to align AI solutions with business objectives and technical requirements. Optimize AI WorkflowsEmploy MLOps practices to ensure scalable, maintainable, and efficient AI model deployment and monitoring Your skills and experience 15+ years of professional experience in AI/ML development, with a focus on agentic AI systems. Proficient in Python, Python API frameworks, SQL and familiar with AI/ML frameworks such as TensorFlow or PyTorch. Experience in deploying AI models on cloud platforms (e.g., GCP, AWS). Experience with LLMs (e.g., GPT-4), SLMs, and prompt engineering. Understanding of semantic technologies, ontologies, and RDF/SPARQL. Familiarity with MLOps tools and practices for continuous integration and deployment. Skilled in building and querying knowledge graphs using tools like Neo4j Hands-on experience with vector databases and embedding techniques. Familiarity with RAG architectures and hybrid search methodologies. Experience in developing AI solutions for specific industries such as healthcare, finance, or ecommerce. Strong problem-solving abilities and analytical thinking. Excellent communication skills for crossfunctional collaboration. Ability to work independently and manage multiple projects simultaneously How we'll support you Training and development to help you excel in your career Coaching and support from experts in your team A culture of continuous learning to aid progression A range of flexible benefits that you can tailor to suit your needs

We are seeking a driven and experienced Nova Context Developer to strengthen the OSS practice for a leading digital solutions company specializing in Cloud, AI-AIOps, product engineering services, and system integration. Key Responsibilities: Actively contribute as a team member on project implementations. Develop, construct, and test components of a Nova Context (Ontology) solution under the supervision of technical leads. Build and maintain strong technical relationships with customers. Support pre-sales efforts when required. Collaborate effectively with internal and client teams to ensure successful project delivery. Continuously develop consulting capabilities and professional competencies. Follow guidance from lead or senior consultants on assigned projects. Key Qualifications & Requirements: Minimum 1 year of hands-on experience in Nova Context (Ontology) solution deployment and construction. 3+ years of experience managing large, data-oriented projects in a customer-facing role. Strong analytical skills to interpret complex datasets, identify patterns, and establish data relationships. Proficient in extracting data from Excel, XML, and using ETL processes. Experience with graph database solutions (NoSQL), RDF, and graph data modeling. Strong command of graph query handling, especially SPARQL and PRONTO (must-have). Advanced scripting and development skills in Python, BASH, Perl, and Linux Shell / CLI. Good understanding of the Telco domain (Wireless: 2G/3G/4G/5G, Wireline: GPON, Fibre, Transport: Microwave, DWDM, SDH, PDH). IT infrastructure knowledge, including virtualization (VMware/MS Hypervisor) and container technologies (Docker, K3s, Kubernetes). Familiarity with data lakes and data modeling techniques. Additional Skills: Strong grasp of SDLC and implementation best practices. Quality-focused with a "completer-finisher" mindset. Business-aware, understanding broader departmental and organizational goals. Self-driven with strong problem-solving skills. Excellent communication and relationship-building skills, including cross-cultural collaboration.

Job Title: Data Science_AI Engineer, AVP Role Description We are seeking a seasoned Data Science Engineer to spearhead the development of intelligent, autonomous AI systems. The ideal candidate will have a robust background in agentic AI, LLMs, SLMs, vector DB, and knowledge graphs. This role involves designing and deploying AI solutions that leverage Retrieval-Augmented Generation (RAG), multi-agent frameworks, and hybrid search techniques to enhance enterprise applications. Your key responsibilities Design & Develop Agentic AI Applications: Utilize frameworks like LangChain, CrewAI, and AutoGen to build autonomous agents capable of complex task execution. Implement RAG Pipelines: Integrate LLMs with vector databases (e.g., Milvus, FAISS) and knowledge graphs (e.g., Neo4j) to create dynamic, context-aware retrieval systems. Fine-Tune Language Models: Customize LLMs and SLMs using domain-specific data to improve performance and relevance in specialized applications. NER Models: Train OCR and NLP leveraged models to parse domain-specific details from documents (e.g., DocAI, Azure AI DIS, AWS IDP) Develop Knowledge Graphs: Construct and manage knowledge graphs to represent and query complex relationships within data, enhancing AI interpretability and reasoning. Collaborate Cross-Functionally: Work with data engineers, ML researchers, and product teams to align AI solutions with business objectives and technical requirements. Optimize AI Workflows: Employ MLOps practices to ensure scalable, maintainable, and efficient AI model deployment and monitoring. Your skills and experience 10+ years of professional experience in AI/ML development, with a focus on agentic AI systems. Proficient in Python, Python API frameworks, SQL and familiar with AI/ML frameworks such as TensorFlow or PyTorch. Experience in deploying AI models on cloud platforms (e.g., GCP, AWS). Experience with LLMs (e.g., GPT-4), SLMs, and prompt engineering. Understanding of semantic technologies, ontologies, and RDF/SPARQL. Familiarity with MLOps tools and practices for continuous integration and deployment. Skilled in building and querying knowledge graphs using tools like Neo4j. For internal use only Hands-on experience with vector databases and embedding techniques. Familiarity with RAG architectures and hybrid search methodologies. Experience in developing AI solutions for specific industries such as healthcare, finance, or e- commerce. Strong problem-solving abilities and analytical thinking. Excellent communication skills for crossfunctional collaboration. Ability to work independently and manage multiple projects simultaneously.