4 Knowledge Graphs Jobs

Role & responsibilities Architect and implement AI/ML solutions tailored to client-specific business problems using Gen AI (e.g., LLMs like GPT, Gemini, Mistral) and traditional ML models (e.g., scikit-learn, XGBoost). Collaborate with client stakeholders to understand requirements, define problem statements, and translate them into scalable AI/ML solutions. Present demos and proof-of-concepts (POCs) to clients, showcasing the value of AI/ML in real-world scenarios Mentor and guide a cross-functional team of data scientists, ML engineers, and developers Drive best practices in model development, deployment, and monitoring Stay abreast of the latest advancements in Gen AI and ML, and evaluate their applicability to client use cases Contribute to internal knowledge bases and reusable solution accelerators Preferred candidate profile Strong programming skills in Python and experience with ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Hands-on experience with Gen AI platforms and LLMs (e.g., OpenAI, Gemini, LLaMA, Mistral). Proven track record of deploying ML models in production environments (cloud/on-prem). Experience with API development, data pipelines, and dashboarding tools like Streamlit. Familiarity with DevOps and MLOps practices for model lifecycle management. Excellent communication and stakeholder management skills. Tools: LLM, SLM, Vector DB, Graph DB, Airflow, MLFlow, MLOps tools, NLP, KG, ML models (Regression, Clustering, Classification etc), LangChain, LangGraph, AutoGen

Ready to shape the future of work At Genpact, we don&rsquot just adapt to change&mdashwe drive it. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos AI Gigafactory, our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to agentic AI, our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, tech-driven environment, love solving real-world problems, and want to be part of a team that&rsquos shaping the future, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation, our teams implement data, technology, and AI to create tomorrow, today. Get to know us at genpact.com and on LinkedIn, X, YouTube, and Facebook. Inviting applications for the role of Senior Principal Consultant- Senior Data Engineer - Databricks, Azure & Mosaic AI Role Summary: We are seeking a Senior Data Engineer with extensive expertise in Data & Analytics platform modernization using Databricks, Azure, and Mosaic AI. This role will focus on designing and optimizing cloud-based data architectures, leveraging AI-driven automation to enhance data pipelines, governance, and processing at scale. Key Responsibilities: . Architect & modernize Data & Analytics platforms using Databricks on Azure. . Design and optimize Lakehouse architectures integrating Azure Data Lake, Databricks Delta Lake, and Synapse Analytics. . Implement Mosaic AI for AI-driven automation, predictive analytics, and intelligent data engineering solutions. . Lead the migration of legacy data platforms to a modern cloud-native Data & AI ecosystem. . Develop high-performance ETL pipelines, integrating Databricks with Azure services such as Data Factory, Synapse, and Purview. . Utilize MLflow & Mosaic AI for AI-enhanced data processing and decision-making. . Establish data governance, security, lineage tracking, and metadata management across modern data platforms. . Work collaboratively with business leaders, data scientists, and engineers to drive innovation. . Stay at the forefront of emerging trends in AI-powered data engineering and modernization strategies. Qualifications we seek in you! Minimum Qualifications . experience in Data Engineering, Cloud Platforms, and AI-driven automation. . Expertise in Databricks (Apache Spark, Delta Lake, MLflow) and Azure (Data Lake, Synapse, ADF, Purview). . Strong experience with Mosaic AI for AI-powered data engineering and automation. . Advanced proficiency in SQL, Python, and Scala for big data processing. . Experience in modernizing Data & Analytics platforms, migrating from on-prem to cloud. . Knowledge of Data Lineage, Observability, and AI-driven Data Governance frameworks. . Familiarity with Vector Databases & Retrieval-Augmented Generation (RAG) architectures for AI-powered data analytics. . Strong leadership, problem-solving, and stakeholder management skills. Preferred Skills: . Experience with Knowledge Graphs (Neo4J, TigerGraph) for data structuring. . Exposure to Kubernetes, Terraform, and CI/CD for scalable cloud deployments. . Background in streaming technologies (Kafka, Spark Streaming, Kinesis). Why join Genpact . Be a transformation leader - Work at the cutting edge of AI, automation, and digital innovation . Make an impact - Drive change for global enterprises and solve business challenges that matter . Accelerate your career - Get hands-on experience, mentorship, and continuous learning opportunities . Work with the best - Join 140,000+ bold thinkers and problem-solvers who push boundaries every day . Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the tech shapers and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

Role & responsibilities : Job Title : AI Engineer (AI-Powered Agents, Knowledge Graphs, & MLOps) Location: Hyderabad Job Type : Full-time Hands-on Gen AI development in GCP and Azure stack Job Summary : We seek an AI Engineer with deep expertise in building AI-powered agents, designing and implementing knowledge graphs, and optimizing business processes through AI-driven solutions. The role also requires hands-on experience in AI Operations (AI Ops), including continuous integration/deployment (CI/CD), model monitoring, and retraining. The ideal candidate will have experience working with open-source or commercial large language models (LLMs) and be proficient in using platforms like Azure Machine Learning Studio or Google Vertex AI to scale AI solutions effectively. Key Responsibilities : AI Agent Development : Design, build, and deploy AI-powered agents for applications such as virtual assistants, customer service bots, and task automation systems using LLMs and other AI models. Knowledge Graph Implementation : Develop and implement knowledge graphs for enterprise data integration, enhancing the retrieval, structuring, and management of large datasets to support decision-making. AI-Driven Process Optimization : Collaborate with business units to optimize workflows using AI-driven solutions, automating decision-making processes and improving operational efficiency. AI Ops (MLOps) : Implement robust AI/ML pipelines that follow CI/CD best practices to ensure continuous integration and deployment of AI models across different environments. Model Monitoring and Maintenance : Establish processes for real-time model monitoring, including tracking performance, drift detection, and accuracy of models in production environments. Model Retraining and Optimization : Develop automated or semi-automated pipelines for model retraining based on changes in data patterns or model performance. Implement processes to ensure continuous improvement and accuracy of AI solutions. Cloud and ML Platforms : Utilize platforms such as Azure Machine Learning Studio, Google Vertex AI, and open-source frameworks for end-to-end model development, deployment, and monitoring. Collaboration : Work closely with data scientists, software engineers, and business stakeholders to deploy scalable AI solutions that deliver business impact. MLOps Tools : Leverage MLOps tools for version control, model deployment, monitoring, and automated retraining processes to ensure operational stability and scalability of AI systems. Performance Optimization : Continuously optimize models for scalability and performance, identifying bottlenecks and improving efficiencies. Qualifications : Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 3+ years of experience as an AI Engineer, focusing on AI-powered agent development, knowledge graphs, AI-driven process optimization, and MLOps practices. Proficiency in working with large language models (LLMs) such as GPT-3/4, GPT-J, BLOOM, or similar, including both open-source and commercial variants. Experience with knowledge graph technologies, including ontology design and graph databases (e.g., Neo4j, AWS Neptune). AI Ops/MLOps Expertise : Hands-on experience with AI/ML CI/CD pipelines, automated model deployment, and continuous model monitoring in production environments. Familiarity with tools and frameworks for model lifecycle management, such as MLflow, Kubeflow, or similar. Strong skills in Python, Java, or similar languages, and proficiency in building, deploying, and monitoring AI models. Solid experience in natural language processing (NLP) techniques, including building conversational AI, entity recognition, and text generation models. Model Monitoring & Retraining : Expertise in setting up automated pipelines for model retraining, monitoring for drift, and ensuring the continuous performance of deployed models. Experience in using cloud platforms like Azure Machine Learning Studio, Google Vertex AI, or similar cloud-based AI/ML tools. Preferred Skills : Experience with building or integrating conversational AI agents using platforms like Microsoft Bot Framework, Rasa, or Dialogflow. Familiarity with AI-driven business process automation and RPA integration using AI/ML models. Knowledge of advanced AI-driven process optimization tools and techniques, including AI orchestration for enterprise workflows. Experience with containerization technologies (e.g., Docker, Kubernetes) to support scalable AI/ML model deployment. Certification in Azure AI Engineer Associate, Google Professional Machine Learning Engineer, or relevant MLOps-related certifications is a plus. Preferred candidate profile Perks and benefits

About the Role: We are seeking a highly skilled Lead Data Scientist with 4-8 years of experience to drive innovation in machine learning, deep learning, and generative AI. The ideal candidate should have expertise in Large Language Models (LLM), Retrieval-Augmented Generation (RAG), AI Agents, and hands-on experience in LangChain or LlamaIndex, and Knowledge Graphs. The role also requires strong technical leadership and experience in AI Agent frameworks and libraries. Key Responsibilities: Lead the development and deployment of machine learning models, deep learning frameworks, and AI-driven solutions across the organization. Work closely with stakeholders to define data-driven strategies and drive innovation using AI and machine learning. Design and implement robust data pipelines and workflows in collaboration with data engineers and software developers. Develop and deploy APIs using web frameworks for seamless integration of AI/ML models into production environments. Mentor and lead a team of data scientists and engineers, providing technical guidance and fostering professional growth. Leverage LangChain or LlamaIndex to enhance model integration, document management, and data retrieval capabilities. Lead projects in Generative AI technologies, such as Large Language Models (LLM), Retrieval-Augmented Generation (RAG), and AI agents, to create innovative AI-driven products and services. Stay updated on the latest AI/ML trends, ensuring that cutting-edge methodologies are adopted across projects. Collaborate with cross-functional teams to translate business problems into technical solutions and communicate findings effectively to both technical and non-technical stakeholders. Required Skills and Qualifications: Experience: 4-8 years of experience in data science and AI/ML, with a strong foundation in machine learning, deep learning, generative AI and data engineering. Generative AI Expertise: Minimum 2 years of experience with gneretaive AI. Hands-on experience with LLMs, RAG, and AI agents. AI Agents & Frameworks: Hands-on experience with AI agent frameworks/libraries (e.g., LangGraph, CrewAI, OpenAI's Function Calling, Semantic Kernel, etc.). Programming: Strong proficiency in Python, with experience using TensorFlow, PyTorch, and Scikit-learn. LangChain & LlamaIndex: Experience integrating LLMs with structured and unstructured data. Knowledge Graphs: Expertise in building and utilizing Knowledge Graphs for AI-driven applications. SQL & NoSQL Databases: Hands-on experience with SQL (PostgreSQL, MySQL, etc.) and NoSQL (MongoDB, Cassandra, etc.) database. API Development: Experience in developing APIs using Flask or FastAPI or Django. Cloud & MLOps: Experience working with cloud providers like AWS, GCP, Azure and MLOps best practices. Excellent communication, leadership, and project management skills. Strong problem-solving ability with a focus on delivering scalable, impactful solutions. Preferred Skills: Experience with Computer Vision applications. Chain of Thought Reasoning: Familiarity with CoT prompting and reasoning techniques. Ontology: Understanding of ontologies for knowledge representation in AI systems. Data Engineering: Experience with ETL pipelines and data engineering workflows. Familiarity with big data tools like Spark, Hadoop, or distributed computing.