Enterprise Architect

Job Summary

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

Key responsibilitiesAI 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

. 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

Other Requirements