Bachelor's or Master's degree in Computer Science, Data Science, AI/ML Engineering, or related field.
5+ years in enterprise IT/applications management and AI/ML solution delivery.
Proven track record leading cross-functional teams on complex AI projects.
Experience with enterprise application platforms like Salesforce, NetSuite, SAP, and Workday.
Demonstrated expertise in GenAI, NLP, RPA, predictive modeling, and computer vision.
Strong understanding of data engineering principles and event-driven architecture.
Experience in regulated environments with compliance standards such as GDPR and SOC 2.
Responsibilities
Define an AI/ML adoption roadmap across various enterprise applications.
Translate strategic objectives into actionable AI initiatives using GenAI.
Advise IT leadership on AI trends and innovations.
Architect AI solutions in Microsoft Azure with integration into enterprise systems.
Lead a team in delivering complex AI projects and implement MLOps practices.
Own AI project delivery from PoC to production, ensuring compliance and security standards.
Collaborate with stakeholders and external vendors for successful project execution.
Benefits
Professional development opportunities including training and mentorship.
Access to cutting-edge technology and tools for AI implementation.
Flexible work environment with options for remote work.
Participation in innovative AI projects impacting business transformation.
Health and wellness programs to support employee well-being.
Full Job Description
Job Description:
Key Responsibilities
AI & Enterprise Application Strategy
Define an AI/ML adoption roadmap across ERP, CRM, HRIS, BI, and custom applications.
Translate strategic objectives into use-case-driven AI initiatives, leveraging GenAI capabilities for tangible business value.
Advise IT leadership on emerging AI trends, frameworks, and platform innovations (e.g., LLM orchestration, multi-modal AI).
Architecture & Integration
Architect end-to-end AI solutions in Microsoft Azure AI, integrating with enterprise systems via REST APIs, GraphQL, and event-driven architectures.
Ensure compatibility with solutions running in AWS SageMaker and hybrid-cloud deployments.
Assist with design data ingestion and preparation pipelines.
CI/CD, MLOps & Team Leadership
Lead a team of engineers and data scientists in delivering complex AI projects (e.g., document intelligence, NLP chatbots, predictive analytics, RPA workflows).
Implement MLOps practices and CI/CD pipelines using GitHub Actions for AI model lifecycle management.
Establish model monitoring, retraining schedules, and drift detection with frameworks like MLflow and Kubeflow.
Project Delivery
Own AI project delivery from PoC to production, ensuring robust governance, risk management, security, and compliance.
Deploy scalable models in Azure AI Studio and productionize via APIs or microservices in Kubernetes/AKS.
Stakeholder & Vendor Engagement
Collaborate with Business Analysts, Product Owners, Developers, and Data Engineers to ensure solutions meet functional and performance requirements.
Partner with external AI vendors, cloud providers, and technology partners to align on deliverables and integrations.
Technical Excellence
Hands-on evaluation and selection of AI/ML frameworks (PyTorch, TensorFlow, scikit-learn) and GenAI orchestration tools (LangChain, Semantic Kernel).
Review and approve solution architecture and code for scalability, efficiency, and security compliance.
Mentor and develop team members through training on AI frameworks, cloud development practices, and architectural patterns.
Governance & Security
Assist with implementation of AI-specific data governance, privacy policies, and responsible AI principles.
Ensure compliance with standards and regulations (GDPR, SOC 2, ISO 27001) and practices such as OAuth2, SAML, RBAC/ABAC, encryption-at-rest/in-transit.
Innovation
Initiate and lead rapid Proofs of Concept (PoCs) and Minimum Viable Products (MVPs) using AI and GenAI for streamlined business processes.
Explore and pilot new AI features in LLMs, vision models, speech-to-text, translation, and personalization engines.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Data Science, AI/ML Engineering, or a related technical field.
5+ years in enterprise IT/applications management with at least 5+ years in AI/ML solution delivery in production environments.
Proven track record leading cross-functional technical teams on complex AI/ML projects in diverse, matrixed enterprise environments.
Deep experience with enterprise application platforms including CRM (Salesforce), ERP (NetSuite, SAP, Oracle), HRIS (Workday), and PSA/Billing (Certinia).
Demonstrated expertise in GenAI, NLP, RPA, predictive modeling, computer vision, and recommendation systems.
Strong understanding of enterprise integration patterns, event-driven architecture, and data engineering principles.
Experience working in regulated or compliance-sensitive environments (SOC 2, GDPR, ISO 27001).
Ability to balance hands-on technical delivery with strategic planning and executive-level communication.
Strong project ownership and accountability with experience in end-to-end delivery from requirements through post-production support.
Technical Requirements
Languages & Frameworks
Advanced Python proficiency including async patterns, data manipulation (pandas, NumPy), and REST API development (FastAPI, Flask).
Working knowledge of Java, C#, or Go for enterprise integrations and microservices development.
Hands-on experience with AI/ML frameworks: TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers.
GenAI orchestration tools: LangChain, Semantic Kernel, LlamaIndex; experience with prompt engineering and RAG architecture design.
Cloud & Infrastructure
Expertise in cloud-native architecture on Microsoft Azure: Azure AI Studio, Azure Machine Learning, Azure OpenAI Service, Azure Data Factory, Synapse Analytics, AKS, Azure Functions.
Hands-on experience with AWS ML services: SageMaker, Bedrock, Lambda, S3, and hybrid-cloud deployment patterns.
Container orchestration: Kubernetes (AKS/EKS), Docker, Helm charts for ML model deployment.
Infrastructure-as-Code: Terraform, Bicep, or ARM templates for reproducible environment provisioning.
Integration & Data
Integration patterns: REST APIs, gRPC, GraphQL, message queues (Kafka, Azure Service Bus, RabbitMQ), and webhook-based architectures.
Data streaming and batch pipeline design using Azure Data Factory, Databricks, Synapse Analytics, and Spark.
Experience designing vector databases and embedding pipelines for RAG/semantic search (Azure AI Search, Pinecone, Weaviate).
Familiarity with data lakehouse patterns and medallion architecture (Bronze/Silver/Gold).
MLOps & DevSecOps
CI/CD pipeline implementation for AI/ML workloads using Azure DevOps, GitHub Actions, or Jenkins.
MLOps platforms: MLflow, Kubeflow, Azure ML Pipelines including model registry, versioning, and experiment tracking.
Model monitoring, drift detection, and automated retraining pipelines.
