Enterprise Architect

• *Job Summary**
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20\+ years in IT, with at least 15\+ years designing and delivering AI/ML solutions at enterprise scale.A proven record of leading multiple AI programs in parallel to production, with cross\-functional teams, budgets and measurable business outcomes.Hands\-on depth across classical ML, deep learning (NLP, computer vision, time series) and GenAI (LLMs, RAG, fine\-tuning, agents).Production experience on at least one major cloud (AWS, Azure or GCP) and its AI/ML services.Experience in client\-facing, consulting or solution\-architect roles, including shaping large engagements and proposals.Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics or a related field. A Master's or PhD is preferred.

• *Key Responsibilities**
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• *Key responsibilities****AI architecture and strategy:** Define the enterprise AI reference architecture and roadmap across ML, deep learning, GenAI, RAG and agentic systems. Make build, buy or partner calls on models, platforms and vendors.**Delivery ownership:** Lead end\-to\-end delivery of multiple concurrent AI programs, from use\-case discovery and proof of concept to production and scale. Own scope, timelines, quality and risk.**Solution design and review:** Architect scalable, secure and cost\-efficient solutions. Review designs for data pipelines, model serving, latency and inference cost.**MLOps and LLMOps:** Set standards for model CI/CD, experiment tracking, feature stores, evaluation, monitoring, drift detection and rollback.**Responsible AI and governance:** Build security, privacy, fairness, explainability and regulatory compliance into every solution. Define guardrails for GenAI use.**Stakeholder engagement:** Work with CXOs, business owners and clients to turn business problems into AI solutions with measurable ROI. Support solution shaping, pre\-sales and executive presentations.**Team leadership:** Lead and mentor data scientists, ML engineers and solution architects. Run the architecture review board and hire for the team.**Innovation and reuse:** Track new research and tools, run proofs of concept, and build reusable accelerators and IP.

• *Skill Requirements**
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### **. ML and Deep Learning**

• Scikit\-learn
• XGBoost
• PyTorch
• TensorFlow
• Natural Language Processing (NLP)
• Computer Vision
• Forecasting
• Recommender Systems

### **2\. GenAI and Agents**

• LLM APIs
• GPT
• Claude
• Llama
• Mistral
• Prompt Engineering
• Fine\-tuning
• LoRA / PEFT
• Retrieval\-Augmented Generation (RAG)
• Vector Databases
• LangChain
• LlamaIndex
• LangGraph
• LLM Evaluation
• Guardrails

### **3\. MLOps and LLMOps**

• MLflow
• Kubeflow
• Amazon SageMaker
• Azure ML
• Vertex AI
• Databricks
• Model Registry
• CI/CD
• Model Monitoring
• Drift Detection

### **4\. Data Platforms**

• Apache Spark
• Apache Kafka
• Databricks
• Snowflake
• Lakehouse Platforms
• Feature Stores
• Data Governance

### **5\. Cloud and Infrastructure**

• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)
• Kubernetes
• Docker
• Terraform
• GPU Optimization
• vLLM
• NVIDIA Triton
• Inference Serving

### **6\. Security and Compliance**

• Identity and Access Management (IAM)
• Data Privacy
• Model Risk Management
• Responsible AI
• GDPR
• EU AI Act

### **7\. Programming Languages**

• *Primary:**

• Python – Expert
• SQL

• *Good to have:**

• Java
• Scala
• Go

• *Must Have Skill**
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Apache Kafka,Gen AI,Machine Learning and Statistical modeling,MySQL,Python

• *Good to Have Skill**
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• *Other Requirements**
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