Description

Key Responsibilities

• Design and define enterprise-scale AI architectures, integrating AI through both disruptive innovation and intelligent infusion into existing business systems and platforms
• Architect multi-layered AI solutions spanning agent orchestration frameworks, MCP (Model Context Protocol) servers, LLM integrations, RAG pipelines, and supporting data infrastructure
• Evaluate and select appropriate AI technologies, models, frameworks, and cloud services based on business requirements, cost, and governance constraints
• Lead pre-sales and solution design engagements—translating complex AI concepts into compelling business value propositions for executive and technical audiences
• Develop solution strategies with clear ROI narratives; actively participate in customer-facing presentations, RFP responses, and solution workshops
• Own the complete solution lifecycle from whiteboard to production—providing architectural leadership through design, development, testing, and deployment phases
• Guide and support implementation teams with hands-on experience, solving real-world engineering challenges as they arise in production environments
• Define standards, patterns, and best practices for AI architecture across the organization and with external partners
• Mentor and upskill technical teams in agentic AI design, multi-model integration, prompt engineering, and responsible AI practices
• Stay current with the rapidly evolving AI landscape—evaluating emerging models, tools, and frameworks and advising on adoption strategy

Required Experience

• 8+ years in enterprise software architecture, with at least 3+ years focused on AI/ML solutions at scale
• Deep knowledge of generative AI, agentic AI systems, and traditional machine learning, including:
• Large Language Models (LLMs) – selection, fine-tuning, prompt engineering, and lifecycle management
• Agentic AI design – agent orchestration, tool use, multi-agent coordination, and MCP server configuration
• RAG (Retrieval-Augmented Generation) – vector databases, embedding models, document grounding, and hybrid search
• Traditional ML – supervised/unsupervised learning, model training pipelines, and MLOps practices
• Hands-on multi-model experience—selecting, integrating, and orchestrating frontier and open-source models including:
• Anthropic Claude (e.g., Claude Sonnet, Claude Opus) for reasoning and complex language tasks
• OpenAI GPT series for generative and agentic use cases
• Google Gemini for multimodal and large context window applications
• Meta Llama and other open-source models for on-premise or cost-optimized deployments
• Ability to benchmark, evaluate, and select the right model for the right task in a production context
• Proven experience with major cloud AI platforms (AWS Bedrock, Azure OpenAI, Google Vertex AI) and associated infrastructure services
• Experience integrating AI solutions with enterprise platforms such as Salesforce, SAP, ServiceNow, Microsoft 365, or similar
• Demonstrated track record of successfully translating AI architectures into delivered, production-grade solutions
• Strong pre-sales capability—experience leading solution workshops, responding to RFPs, and presenting to senior decision-maker

Technical Competencies

• Enterprise AI architecture patterns and multi-layered solution design
• Agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, watsonx Orchestrate) and MCP protocol-based integrations
• Prompt engineering, chain-of-thought reasoning, and advanced LLM interaction patterns
• Vector databases (e.g., Pinecone, Weaviate, pgvector, Chroma) and semantic search architecture
• Cloud-native and hybrid deployment architectures across AWS, Azure, GCP, and on-premise environments
• API design, microservices, event-driven architecture, and enterprise system integration patterns
• AI governance, responsible AI frameworks, model risk management, bias detection, and explainability
• AI observability and monitoring—model drift detection, performance tracking, data quality, and inference health
• Data architecture, data pipelines, and governance in the context of AI workloads
• Security and compliance considerations for enterprise AI deployments

Soft Skills & Leadership Qualities

• Exceptional communication and presentation skills—able to engage C-suite executives, business stakeholders, and deep technical teams with equal confidence
• Strong sales acumen and solution storytelling ability—can craft a compelling narrative around AI value and guide customers through complex solution decisions
• Strategic thinker with a consistent focus on business outcomes over technical novelty
• Proven ability to lead through influence in matrixed enterprise environments without direct authority
• Comfortable navigating ambiguity and driving clarity—from early-stage concept to production delivery
• Confident public speaker—comfortable presenting at customer briefings, industry events, and executive forums
• Collaborative and empathetic leader—able to mentor, inspire, and upskill diverse technical teams

Nice to Have

• Relevant AI/cloud certifications (AWS Certified Machine Learning, Azure AI Engineer, Google Professional ML Engineer, or equivalent)
• Experience with AI governance platforms and model observability tooling (e.g., Fiddler, Arize, Arthur AI, or similar)
• Background in enterprise consulting, solution architecture, or pre-sales engineering roles
• Published thought leadership—blog posts, whitepapers, conference presentations, or open-source contributions in the AI space
• Experience scaling AI solutions across multiple business units or global enterprise environments
• Exposure to regulated industries (financial services, healthcare, government) where AI governance and compliance are mission-critical
• Familiarity with AI safety, alignment principles, and emerging regulatory frameworks (EU AI Act, NIST AI RMF)

Key Skills

Education

Any Gradute