Requirements
Bachelor of Computer Science or Computer Engineering or relevant degree with specialization and certification in AI, ML engineering and/or Data Science. Minimum of 8 years relevant experience in technical implementation of AI solutions, designing data architectures, developing AI models, and ensuring the technical robustness of AI systems. Experience with machine learning techniques, such as Supervised/Unsupervised Learning, Deep Learning (e.g., Neural Networks, CNNs, RNNs), Natural Language Processing (NLP), Computer Vision and Text Analytics. Hands-on experience with data agents, operational agent and Agentic AI. Proven track record of delivering successful AI /Data digital products in the industry. Experience in AI Ready Data platforms & Data architecture and data engineering foundation. Strong understanding of software development life cycle (SDLC) and DevOps Practices. Strong hands-on Experience in AI and ML architecture, including Agentic and autonomous AI & ML Ops Life cycle. Strong Experience of design pipelines for model training, evaluation, and retraining including CI/CDD. Excellent problem-solving skills and attention to detail. Strong communication and teamwork abilities. Ability to work independently. Experience with agile tools and software, such as Jira, Confluence, or similar. Experience on Projects using Industry Data Platforms (such as OSDU or Cognite Date Fusion) would be a plus.
Description
Job description:Key Accountabilities This will be a definite term contract for 3 years. Evaluate, test, design and enable technology selection and implementation approach to improvement and optimize capabilities and data architecture (including AI & ML) and design of agentic and autonomous AI solutions for enterprise use cases (covering POC, scale-up, and production deployment)Evaluate, test, and design end to end data and AI architectures for enterprise and agentic use cases, covering POC, scale up, and production deployment.Architect enterprise data platforms optimized for AI, ML, and LLM integration and inference at scale.Define AI ready data architecture patterns supporting fine tuning, retrieval augmented generation, and prompt engineering workflows.Design hybrid architectures integrating on premises and cloud based LLM services with enterprise data platforms.Define reference patterns for multi model AI environments operating on shared and governed data. Design semantic layers, indexing, and similarity search for low latency AI workloads.Design enterprise knowledge graph architectures to support reasoning and contextual understanding.Define architectural mechanisms for data refresh, accuracy, relevance, and observability of retrieval performance and latency.Design multi agent architectures for cross functional workflows, including decision logic, tool usage, and interaction patterns for autonomous agents such as Microsoft Fabric Data Agent and Operational Agent.Design guardrails, auditability, and control mechanisms for agent actions, including feedback loops for continuous improvement.Define prompt orchestration patterns for multi-step reasoning aligned with NOC standards.Assess AI models, platforms, and tools using defined evaluation criteria and benchmarking, including proprietary, commercial, and open source options.Review and provide guidance on AI, ML, and advanced analytics architecture artefacts from vendors.Job PurposeResponsible for designing and advancing data solutions that strengthen NOC data architecture and evaluating the adoption of emerging technologies within DBS, with a focus on AI, machine learning, and advanced analytics. The role requires deep expertise in Data & AI Architecture /Engineering, cloud platforms, and integration patterns, combined with strong Oil & Gas domain knowledge. It aims to enhance enterprise analytics capabilities by integrating AI/ML models and large-scale data processing frameworks to enable data-driven decision-making and improve operational efficiency. Key Accountabilities:Evaluate, test, design and enable technology selection and implementation approach to improvement and optimize capabilities and data architecture (including AI & ML) and design of agentic and autonomous AI solutions for enterprise use cases (covering POC, scale-up, and production deployment)Provide architectural guidance for data integration, ingestion patterns across enterprise data platforms and advanced analytics solutions in alignment with data modelling and MDM standardsDesign solutions in alignment with DBS best practices on Data and AI governance, information security controls, EA and PMO standardsReview solutions and demands impacting Data Management part of Architecture Review Board (ARB)meetingsKey AccountabilitiesProvide architectural guidance for data integration, ingestion patterns across enterprise data platforms and advanced analytics solutions in alignment with data modelling and MDM standards Design batch and streaming or real time ingestion and integration patterns aligned with NOC standards, including Integration Design Documents and design first principles.Define medallion, Kappa, and Lambda architectures as reference patterns.Maintain NOC data architecture for data platforms, digital use cases, demands, and data solutions. Document reference architectures and standard solution patterns for data and AI solutions, including IDD, HLD, LLD, dashboards and reports, model representations, data dictionaries, and data models such as CDM,LDM, and PDM. Ensure alignment of MDM principles with analytics and AI data requirements.Design data pipelines supporting continuous model training, evaluation, deployment, and retraining.Design CI and CD flows for ML models with monitoring for accuracy, latency, and cost.Design cost efficient inference and fine-tuning pproaches with usage tracking and controls..Design solutions in alignment with DBS best practices on Data and AI governance, information security controls, EA and PMO standardsSupport alignment with data and AI governance to ensure trusted and well governed data usage across platforms and advanced analytics.Design privacy preserving architectures for model training and fine tuning.Support definition of security controls and access patterns for data and AI platforms.Review solutions and demands impacting Data Management part of Architecture Review Board (ARB) meetings Support Architecture Review Board and Design Advisory sessions for data, AI, and ML solution reviews and approvals.Support delivery teams through architecture reviews and structured feedback on design artefacts.Identify and review reference solutions, architectures, and case studies to improve NOC architecture.Provide technical guidance to data operations, data governance, and related teams as required. CompetenciesDelegate Appropriately and Communicate Effectively Demonstrate Adaptability in Managing Complex Situations Demonstrate Learning Agility and Drive Innovation Develop Emotional Intelligence and Cultural Competence Develop Others and Institutionalise Knowledge Develop Talent for the Future NOC Sustainability Facilitate Collaboration and Sense of Community Improve Safety, Technical Proficiency and Results Lead By Example with NOC Values Set Purpose, Strategic Direction and Company Plan Strengthen Accountability and Continuous Improvement Think and Act in Stakeholder-Centric Ways Education Bachelor of Computer Science or Computer Engineering or relevant degreeSpecialization and certification in AI, ML engineering and/or Data Science.Experience Minimum of 8 years relevant experience in technical implementation of AI solutions. Designing data architectures, developing AI models, and ensuring the technical robustness of AI systems.Experience with machine learning techniques, such as Supervised/Unsupervised Learning, Deep Learning (e.g., Neural Networks, CNNs, RNNs), Natural Language Processing (NLP), Computer Vision and Text AnalyticsHands on Experience with data agents, operational agent and Agentic AIProven track record of delivering successful AI /Data digital products in the industry.Job-Specific Skills (Generic / Technical):Experience in AI Ready Data platforms & Data architecture and data engineering foundation, technicalimplementation of AI solutions. Designing data architectures, developing AI models, and ensuring thetechnical robustness of AI systems.Strong understanding of software development life cycle (SDLC) and DevOps Practices.Strong hands-on Experience in AI and ML architecture, including Agentic and autonomous AI & ML Ops Life cycle includes running POC to scale up solutions Strong Experience of design pipelines for model training, evaluation, and retraining including CI/CDDExcellent problem-solving skills and attention to detail.Strong communication and teamwork abilities.Ability to work independently.Experience with agile tools and software, such as Jira, Confluence, or similar.Experience on Projects using Industry Data Platforms (such as OSDU or Cognite Date Fusion) would be a plus