Role Summary:

The Director is a senior, client-facing technical leader responsible for driving architecture, solution direction, Value Engineering (ROI analysis), and technical governance across ML, AI, and GenAI pursuits.

This role bridges the gap between technical complexity and business value by translating enterprise business challenges into scalable, commercially viable, and production-ready AI solutions.

The role combines deep expertise in the AI ecosystem with strong client engagement skills to shape winning technical strategies, guide architecture decisions, lead POCs and demos, and ensure that proposed solutions are technically sound, operationally feasible, and aligned to measurable client outcomes.

The person in this role will mentor and guide a team of AI pre-sales architects and technical leads, raising the overall technical quality, business alignment, and delivery readiness of the pre-sales function.

The Director will partner with delivery leaders, sales, finance, and cloud/platform partners to provide technical inputs for effort estimation, feasibility assessment, ROI alignment, and solution shaping, while commercial ownership remains with the appropriate business and delivery leadership.

1. Pre-sales Solution Architecture and Technical Leadership:

- Own the end-to-end AI and GenAI solution architecture across the pre-sales lifecycle, including RFPs, RFIs, proactive proposals, and strategic client pursuits.

- Lead technical discovery sessions, whiteboarding workshops, and architecture discussions with client stakeholders to understand business context, technical constraints, and success criteria.

- Translate business objectives into robust AI solution architectures, reference architectures, and implementation approaches.

- Provide technical direction and architectural governance for all AI pre-sales pursuits, ensuring solutions are scalable, secure, maintainable, and aligned to enterprise standards.

- Partner with sales and delivery teams to shape the solution approach and ensure technical feasibility.

- Design architectures with a focus on Total Cost of Ownership (TCO), including token optimization, model selection strategies, capacity planning, infrastructure efficiency and AI operational cost governance to prevent bill shock.

- Develop competitive win themes by analyzing technical approaches and positioning proprietary accelerators, reusable assets, and solution differentiators as superior alternatives.

2. Hands-on PoCs, Demos, and Validation:

- Lead the design and delivery of high-impact PoCs, demos, and technical spikes that validate solution feasibility and help de-risk client decisions.

- Direct and critically review the development of high-impact PoCs, ensuring solutions are not only innovative but also production-ready, measurable, and aligned to specific business KPIs.

- Be prepared to get hands-on when needed, including building prototype-grade code, notebooks, workflows, or reference implementations to prove architectural concepts.

- Create reusable demo assets, accelerators, and proof assets that can be tailored across industries and client scenarios.

- Ensure demos and PoCs are rooted in realistic data, use cases, and business outcomes to demonstrate practical value.

- Capture lessons learned from PoCs and demos to continuously improve reusable assets and solution patterns.

3. Modern AI and GenAI Architecture:

- Design enterprise-grade AI architectures across data ingestion, model orchestration, retrieval, prompting, evaluation, safety, observability, and deployment layers.

- Define solution patterns using frameworks such as LangChain, LlamaIndex, LangGraph, or equivalent approaches for agentic workflows.

- Architect advanced RAG and hybrid retrieval solutions using vector stores, search systems, and optionally knowledge graphs for enterprise reasoning use cases.

- Contribute architecture guidance for predictive ML, NLP, deep learning, multimodal AI, and GenAI use cases across industries.

- Ensure solutions leverage cloud-native AI services and platforms such as Azure OpenAI, Azure AI, AWS Bedrock, SageMaker, and GCP Vertex AI.

- Architect for the full LLM lifecycle, including evaluation frameworks, observability, prompt/version management, telemetry, and continuous feedback loops for model performance optimization.

- Design solutions that account for data residency, sovereignty, and compliance requirements, particularly for regulated industries such as Pharma, Fintech, Healthcare, and Retail.

4. Technical Governance and Responsible AI:

- Establish and enforce technical guardrails, architecture review standards, and solution governance across AI pre-sales engagements.

- Ensure proposed solutions address security, privacy, compliance, observability, traceability, and operational readiness.

- Incorporate Responsible AI principles including fairness, transparency, explainability, bias mitigation, and responsible handling of sensitive data.

- Guide the inclusion of safety mechanisms such as PII redaction, prompt/output controls, hallucination mitigation, and auditability.

- Ensure pre-sales solutions are production-aware and compatible with downstream delivery and MLOps practices.

5. Delivery Alignment and Estimation Support:

- Collaborate closely with delivery leaders to validate feasibility, assumptions, and technical dependencies for proposed solutions.

- Provide technical inputs into effort estimation, delivery phasing, and implementation planning.

- Ensure clear handoff of architecture artefacts, assumptions, constraints, and risks from pre-sales to delivery.

- Support smooth transition from solution shaping to implementation by maintaining architectural integrity and clarity throughout the pursuit.

- Work with cross-functional stakeholders to ensure solutions are ambitious but technically grounded.

6. Team Leadership and Capability Building:

- Lead and mentor AI pre-sales solution architects and technical leads, improving their architectural depth, client engagement skills, and solution quality.

- Raise the technical standard of the pre-sales function through playbooks, reusable patterns, review frameworks, and best practices.

- Support coaching, feedback, and capability growth for team members involved in solutioning, demos, PoCs, and client workshops.

- Contribute to the maturity of the AI pre-sales practice by sharing lessons learned and standardizing repeatable approaches.

- Act as a senior technical role model for both client-facing and internal architecture excellence.

7. Market Awareness and Thought Leadership:

- Stay current on emerging AI and GenAI technologies, enterprise adoption patterns, and evolving architecture practices.

- Contribute ideas from client engagements and market trends to internal solution roadmaps and reusable offerings.

- Participate in thought leadership activities such as internal knowledge sessions, webinars, blogs, or client workshops.

- Translate emerging trends into practical, client-relevant architectural recommendations.

8. Partner & Ecosystem Strategy:

- Collaborate with Tier-1 cloud partners such as Azure, AWS, and GCP, along with AI ecosystem platforms to build better together solution narratives.

- Leverage co-sell programs, joint GTM initiatives, partner funding opportunities for PoCs, and strategic alliance programs to strengthen solution positioning.

- Drive adoption of emerging capabilities by participating in partner early-access programs and private preview initiatives for AI/GenAI technologies.

- Align partner capabilities, accelerators, and ecosystem offerings into scalable client solutions and reusable enterprise patterns.

Required Experience and Qualifications:

- 15 to 20+ years of experience in technology, with a strong background in AI/ML, GenAI, enterprise solution architecture, or digital transformation consulting.

- Significant experience in client-facing pre-sales, solution architecture, or consulting roles involving enterprise technology pursuits.

- Proven track record of supporting large-scale enterprise deals where technical architecture and solution differentiation were primary decision drivers.

- Deep technical expertise in AI/ML, NLP, GenAI, deep learning, prompt engineering, RAG, orchestration patterns, and agentic AI architectures.

- Strong architectural understanding of cloud-native AI services and platforms such as Azure, AWS, or GCP.

- Practical familiarity with MLOps, LLMOps, deployment patterns, observability, APIs, containerization, and scalable distributed system design.

- Hands-on capability to build or critically review PoCs, prototypes, and reference implementations when needed.

- Experience working with cross-functional teams including sales, delivery, engineering, finance, and executive leadership.

- Strong communication, storytelling, and executive presence, with the ability to pivot seamlessly between deep technical whiteboarding sessions and business-value-driven CXO presentations.

- Experience in one or more industry domains such as Retail, Pharma, Loyalty, Fintech, Healthcare.

- Proven ability to mentor technical teams and lead by influence in a consulting or pre-sales environment.

Preferred Qualifications:

- Experience leading or mentoring AI solution architects, pre-sales engineers, or technical leads.

- Experience with multimodal AI, agentic workflows, GraphRAG, or enterprise knowledge systems.

- Exposure to Responsible AI, fairness, governance, and model risk management practices.

- Familiarity with proposal development, proposal defense, and executive client engagement.

- Familiarity with AI practices, including optimization and governance of operational AI/GenAI costs at scale.