8 AI/ML Solution Architecture and Design Jobs

• Full Time

• 10 - 20 Years

• AI/ML Solution Architecture and Design
• Assessment & implementation of Regulatory Standards
• Databricks Machine Learning
• MLOps
• Regulatory Framework Development & implementation
• Requirement Gathering
• Robo Investing
• SAS AML
• Trade Finance - Receivables Finance

ResponsibilitiesProvide SME input on AML investigation workflows, alert review, suspicious activity assessment, narrative quality, evidence requirements and reviewer approval processes.Define functional requirements, business rules, sample scenarios, role personas, operational workflows and acceptance criteria for AML-related product capabilities.Support design…

• Full Time

• 5 - 20 Years

• AI/ML Solution Architecture and Design
• Computer Vision
• Computer Vision with Azure
• mlflow
• Python

ResponsibilitiesDesign, train, and optimize deep learning models for computer vision tasks such as object detection, image recognition, segmentation, and video analytics.Lead large-scale real-time implementations involving 100+ video streams.Fine-tune models like CNNs and Vision Transformers using frameworks such as…

• Full Time

• 8 - 15 Years

• Agile Testing - ALL
• AI/ML Solution Architecture and Design
• Architecture - ALL
• chains
• Conversational AI Platform
• Databricks AI Engineering Services
• Digital Architecture
• Generative AI for Data Analytics
• LLMOps
• MLOps
• Model Optimization
• Model Support
• Prompt Engineering

ResponsibilitiesAI Architecture & EngineeringDefine and own AI reference architectures for generative AI, agentic systems, and AI augmented applicationsArchitect scalable solutions using LLMs, multi agent systems, orchestration frameworks, and AI pipelinesDesign AI platforms supporting model serving, prompt management, RAG,…

• Full Time

• 8 - 15 Years

• AI/ML Solution Architecture and Design
• Architecture - ALL
• Data Architecture - Metadata Management
• Databricks
• Databricks AI Engineering Services
• Databricks Machine Learning
• Digital Architecture
• IBM Infosphere Datastage - Datastage
• LLMOps

ResponsibilitiesKey ResponsibilitiesData Architecture for AIArchitect AI data foundations including ingestion, transformation, enrichment, and serving layersDesign data architectures supporting RAG, embeddings, feature stores, and training data pipelinesDefine standards for data quality, lineage, versioning, and governance for AI workloadsEnsure data…

• Full Time

• 8 - 15 Years

• AgentOps
• AI/ML Solution Architecture and Design
• Artificial Intelligence - ALL
• ETL & Data Quality - ALL
• explainable ai
• Generative AI - Basic
• LLMOps
• MLOps
• Quality Engineering Strategy Design
• Test Scripting
• Verification & Validation

ResponsibilitiesLead AI-led technology consulting across SRE, DevOps, and Data Intelligence domains — evaluating client quality ecosystems, identifying modernisation opportunities, and producing AI-driven technology strategy and blueprints that embed intelligence into every quality touchpoint from code commit to production…

• Full Time

• 11 - 20 Years

• Agile
• Agile Testing - ALL
• AI/ML Solution Architecture and Design
• Scrum
• Scrum master

ResponsibilitiesRoles and Responsibilities:AI-integrated, AI/ML & Generative AI Focus, Generative AI tools, AI/ML, MLOps, and Generative AIDiagnose current state of Agile practices and suggest improvement actionsConsulting the clients to Define an Agile adoption/transformation strategy at various levels- enterprise, portfolio,…

• Full Time

• 2 - 5 Years

• AI/ML Solution Architecture and Design

ResponsibilitiesKey ResponsibilitiesDesign, develop, and deploy machine learning and AI modelsBuild and maintain data pipelines for training and inferenceWork on supervised and unsupervised learning algorithmsImplement NLP, Computer Vision, or Deep Learning solutionsOptimize models for performance, scalability, and accuracyCollaborate with…

• Full Time

• 3 - 12 Years

• AI/ML Solution Architecture and Design
• MLOps
• Prompt Engineering

ResponsibilitiesPython, data structures, basic software engineering discipline (clean code, testing)ML fundamentals: supervised/unsupervised learning, metrics, bias/variance, evaluationData handling: SQL, data quality, feature understandingGit, basic CI/CD awarenessCloud basics (any of AWS/Azure/GCP), containers (Docker) basics Additional ResponsibilitiesDistributed computing: Spark, RayData engineering…