Enterprise Architect
About this role
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 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
. 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