knowledge graph developper

Roles & Responsibilities

• Design and build ontologies (knowledge models) that organize Dell's business information in a structured way.
• Connect data from different systems so that all information is consistent and easy to understand.
• Create relationships between products, customers, services, and other business data to improve search and AI capabilities.
• Work closely with business teams to understand their requirements and convert them into structured knowledge models.
• Ensure data is organized according to industry standards such as RDF, OWL, and SPARQL.
• Improve enterprise search, analytics, and AI-driven applications by maintaining accurate knowledge structures.
• Collaborate with developers, data engineers, AI teams, and business stakeholders on knowledge management projects.
• Manage and update ontologies as new products, services, and business information are introduced.
• Support knowledge graph and metadata initiatives to improve data quality and consistency.
• Use graph databases and ontology management tools (where applicable) to maintain semantic data models.

Ideal Candidate

• Strong Ontologist Profile with deep semantic (RDF) ontology experience in complex enterprise environments
• Mandatory (Experience 1) – Must have 7+ years of total experience, with 5+ years in semantic (RDF) ontology building/structuring for a complex/large enterprises
• Mandatory (Experience 2) – Must have a strong understanding of content and information management, data structures, and semantic metadata extraction
• Mandatory (Tech skill 1) – Must have strong experience with graph databases, taxonomies, and NLP strategies for optimization
• Mandatory (Tech skill 2) – Must have strong working experience of ontology/TOMS tools such as PoolParty (Semantic Web), TopQuadrant EDG, or GraphDB (Ontotext)
• Mandatory (Tech skill 3) – Must be familiar with W3C industry standards, including SPARQL and OWL.
• Mandatory (Communication) – Must be fluent in English, with strong stakeholder management skills to operate across a complex, multi-team environment.many stakeholders.
• Mandatory (Note) – Must be available to work during US time zones for at least 4 hours per day
• Preferred (Education) – Must have a degree in Computer Science or Library Science, preferably with a minor / concentration / certificate in Information Management or Library Automation
• Preferred (Tech skill 3) – Understanding of content development and search/retrieval tools.

Company: ContexQ

Location: Singapore / India / Malaysia (Remote/Hybrid)

At ContexQ, we are fundamentally reshaping the emerging landscape of Contextual Decision Intelligence. We leverage cutting-edge composite AI, Vector Search, and advanced graph technology to resolve billions of entities continuously. By uncovering hidden relationships, we revolutionize how global enterprises handle fraud detection, financial crime (AML), risk management, and supply chain intelligence.

We are an ambitious, fast-moving startup taking on massive enterprise big data challenges, and we are looking for a Founding Knowledge Graph Scientist to push the boundaries of what is possible in semantic data resolution and network graph AI.

This is not a traditional corporate research role. We are looking for an elite AI researcher and practitioner with a PhD and the DNA of an entrepreneur—someone who can architect novel Graph Transformer architectures, develop cutting-edge explainable AI (XAI) models for fraud detection, and translate deep academic expertise into high-impact enterprise solutions. You will be at the forefront of designing the intelligence engine that protects global financial institutions from complex, coordinated financial crimes.

THE MISSION

As our Founding Knowledge Graph Scientist, you are the intellectual and technical force behind our core AI reasoning engine. You will bridge the gap between state-of-the-art academic research in graph neural networks and the practical, high-throughput demands of enterprise anti-money laundering (AML) and fraud detection systems.

Your ultimate metric is the accuracy, efficiency, and explainability of our graph-based AI models. You will architect proprietary knowledge graph structures, implement advanced Graph Transformer models (like FraudGT or similar architectures), and create the analytical "awe factor" that proves to C-suite banking executives that ContexQ can catch financial crime that legacy systems miss.

WHAT YOU WILL DO

• Architect the Graph: Design and optimize large-scale financial knowledge graphs that capture complex temporal, spatial, and transactional relationships across billions of nodes and edges to detect collusion and laundering behaviors.

• Pioneer AI Models: Develop, train, and deploy advanced Graph Neural Networks (GNNs) and Graph Transformers specifically tailored for highly imbalanced, large-scale financial data.

• Drive XAI in FinCrime: Champion model transparency by building explainable AI mechanisms that allow compliance officers to understand exactly why a transaction or entity was flagged, ensuring strict adherence to global banking regulations.

• Bridge Research & Product: Act as the crucial link between theoretical AI research and our core product roadmap. Publish findings, stay ahead of state-of-the-art fraud detection methodologies, and directly implement these breakthroughs into the ContexQ platform.

• Collaborate Across Teams: Work seamlessly with Data Engineering, MLOps, and Business Analysts to ensure your graph models are deployed efficiently, scale securely, and directly address the pain points of our enterprise clients.

WHO YOU ARE

• The Deep Expert: You hold a PhD in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, or a closely related field, with a heavy research focus on Knowledge Graphs, Graph Neural Networks, or Graph Transformers.

• Domain Specialized: You possess deep expertise (either academic or applied) in utilizing graph analytics for anomaly detection, specifically within the realms of Financial Crime, Fraud detection, or cybersecurity.

• A Proven Builder: You have a track record of translating complex AI theory into functional code (PyTorch, PyTorch Geometric, DGL). You are not just a theoretician; you can build, test, and optimize models for real-world deployment.

• A Master Communicator: You can distill complex mathematical concepts and graph topologies into clear, compelling narratives for both highly technical engineering teams and non-technical business stakeholders.

COMPENSATION & THE CONTEXQ UPSIDE

We are explicitly looking for top-tier research and engineering talent who wants to bet on themselves, act as true owners, and build foundational technology.

We structure compensation to heavily reward dedication, innovation, and long-term impact. In exchange for your deep expertise and technical leadership, we offer competitive compensation, significant early-stage foundational equity worth up to $1,000,000 USD over time, a high-impact remote-first culture, and a structure designed to directly align your wealth creation with the platform's enterprise valuation.

This role is designed for an AI pioneer who wants real skin in the game. If you are looking for a purely academic sandbox with no commercial pressure, this isn't for you. If you want the massive upside of building an enterprise AI juggernaut from the ground up and sharing directly in the value you create by stopping global financial crime, you belong at ContexQ.

🚨 Hiring – Data Scientist | Python + Agentic AI

💼 Experience: 5+ Years

Must Have:

• Strong Data Science experience

• Python

• Agentic AI / AI Agents

• Generative AI / LLMs

• RAG / Vector Databases

• LangChain / LangGraph or similar Agent Frameworks

• Machine Learning & NLP

Dear Candidate,

We have an immediate opening with Mphasis for Senior Full Stack Engineer (Python AI).

📍 Location: Hyderabad / Bangalore

💼 Experience: 5+ Years

🏢 Employment: Permanent

⚡ Immediate Joiners Preferred

Mandatory Skills:

• Python
• Java + Spring Boot
• React.js
• MongoDB

GenAI

We are looking for a Data Science & Machine Learning Senior Associate with 3–5 years of relevant experience in data science, machine learning, and analytics. The candidate will be responsible for developing predictive models, analyzing complex datasets, building scalable ML solutions, and supporting production-grade data science applications on cloud platforms.

• Develop and implement machine learning and predictive analytics models.
• Perform data analysis, statistical modeling, and optimization to solve business problems.
• Build demand forecasting and predictive models using time-series and other advanced techniques.
• Work with large datasets using Python, SQL, PySpark, and cloud-based data platforms.
• Develop and maintain scalable data pipelines for ML model development and deployment.
• Implement MLOps practices including model deployment, monitoring, retraining, and data-drift detection.
• Collaborate with software engineers, product teams, and business stakeholders to convert business requirements into analytical solutions.
• Validate models for accuracy, robustness, bias, and production readiness.
• Create meaningful visualizations and communicate analytical insights to technical and non-technical stakeholders.

• Python
• SQL
• Data Science & Machine Learning
• Predictive Modeling
• Statistical Analysis
• Machine Learning Algorithms
• Optimization Techniques
• Google Cloud Platform (GCP)
• BigQuery
• Dataflow
• Dataproc
• Data Fusion
• Cloud SQL
• Airflow
• PySpark
• PostgreSQL
• Terraform
• Tekton
• APIs
• MLOps

• Java

• Demand Forecasting
• Time-Series Analysis
• Neural Networks
• Ensemble Methods
• Support Vector Machines (SVM)
• Regression and Cluster Analysis
• ML Model Testing
• Bias Detection and Data Drift Monitoring
• Production ML Deployment
• QlikSense
• Automotive or Supply Chain Analytics

Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a related technical field.Master's degree in a relevant quantitative or technical field is preferred.

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 28 Years

1st virtual , 2nd round F2F

Python pyspark, SQL, data engineer

5+yrs

Bang/hyderabad

immediate to 15days.

• 10+ years of software development experience
• 3+ years in a technical leadership role
• Strong expertise in Python and SQL
• Experience building scalable APIs and backend systems
• Solid understanding of database design and performance tuning
• Experience with Azure cloud services (AWS familiarity preferred)
• Working knowledge of ML/AI integration in enterprise systems
• Experience in client-facing or consulting environments preferred
• Experience with Databricks or modern data platforms
• Exposure to ETL tools such as Talend
• Experience with BI tools (e.g., Power BI)
• Exposure to regulated domains such as Pharma, Healthcare

1st virtual , 2nd round F2F -

Python pyspark, SQL, data engineer

5+yrs

Bang/hyderabad

immediate to 15days.

Title                                 : Senior GenAI Engineer — RAG (Full-Stack)

Experience                    : 5+ years

Location                         : Remote

Work type                      : Chennai - Work from Office/ Remote – Other locations

Employment Type      : Full Time

Notice Period               : Immediate

Work Day                       :Mon to Fri

Key Responsibilities:

• RAG pipeline end to end: ingestion integration, hybrid retrieval with reranking, prompt/context strategy, citation resolution, refusal behavior
• Permission-aware retrieval: source ACL mapping (SharePoint/Entra, Confluence) to fail-closed retrieval filters; zero-leakage test suite partnership with QA
• Vector database design and operations (Milvus or pgvector): schema, metadata filters, sync, performance
• Full-stack product build: React/TypeScript chat and citation experience, Python/FastAPI services, REST APIs, SSO/OIDC integration, admin configuration UI
• Evaluation-driven development: retrieval precision, faithfulness, and citation-accuracy metrics as the daily working loop; A/B testing of retrieval and prompt variants
• Latency engineering to the 3–5s first-token / ~15s complete-answer targets at concurrency

Technical Skills:

• 5+ years software engineering with 2+ years building RAG/LLM applications in production — with real users and real quality metrics, not notebooks
• Deep retrieval craft: chunking strategy, embeddings, hybrid search, rerankers; you can explain why retrieval fails and how you measured the fix
• Genuine full-stack evidence: shipped React/TypeScript front ends AND Python back-end services in production; API design; OIDC/SAML integration
• Vector database production experience (Milvus, pgvector, Weaviate, or equivalent) including permission/metadata filtering
• Evaluation fluency: has built or operated a retrieval/answer quality harness with numeric thresholds

Strongly Preferred:

• Permission-aware/multi-tenant retrieval specifically; Microsoft Graph API; NIM/OpenAI-compatible serving endpoints; streaming UX; enterprise design systems; banking content domains

About Ampera: 

Ampera Technologies, a purpose driven Digital IT Services with primary focus on supporting our client with their Data, AI / ML, Accessibility and other Digital IT needs. We also ensure that equal opportunities are provided to Persons with Disabilities Talent. Ampera Technologies has its Global Headquarters in Chicago, USA and its Global Delivery Center is based out of Chennai, India. We are actively expanding our Tech Delivery team in Chennai and across India. We offer exciting benefits for our teams, such as 1) Hybrid and Remote work options available, 2) Opportunity to work directly with our Global Enterprise Clients, 3) Opportunity to learn and implement evolving Technologies, 4) Comprehensive healthcare, and 5) Conducive environment for Persons with Disability Talent meeting Physical and Digital Accessibility standards