Roles & Responsibilities :

Shape demand and executive conviction :

- Lead discovery and architecture workshops with client business and technology leadership; set the agenda rather than respond to it.

- Identify pain points, value levers, and high-impact AI opportunities, and sequence them into a pragmatic AI roadmap.

- Translate ambiguous, complex client questions into solution hypotheses, target-state architecture, and a phased path to get there.

- Build AI scenarios and value frameworks that illustrate meaningful business transformation, with measurable outcomes and a defensible business case.

Own the architecture :

- Own end-to-end architecture : LLM and model-portfolio selection, context engineering, retrieval and knowledge architecture, agent orchestration, microservices, integrations, data platform touchpoints, and UI.

- Set the non-functional bar and design to it - latency, throughput, unit cost per interaction, accuracy/eval thresholds, observability, safety, security, data residency, and auditability.

- Define reference architectures, agentic blueprints, and accelerator standards for the practice.

- Make and document build-vs-buy and platform decisions, including model routing, fine-tuning vs prompting vs retrieval, and open vs proprietary model strategy.

Prove it :

- Build prototypes, storyboards, demos, accelerators, and reusable agentic blueprints.

- Engineer and validate AI agents for reliability, reasoning, safety, and performance; establish the evaluation and red-teaming approach.

- Lead PoCs, pilots, and phase-0 MVPs across industries, arbitrating scope so the proof lands inside the sales cycle.

Win and hand over :

- Author and quality-gate client-ready content : architecture decks, SoWs, proposals, estimates, value frameworks, risk and assumption registers, and demo storylines.

- Lead technical deep-dives, security and architecture reviews, and competitive differentiation discussions.

- Refine solution narratives based on client feedback and market conditions.

- Oversee a seamless transition from sales to delivery.

Build the practice :

- Create thought leadership and reusable IP; represent the firm externally at client forums, partner events, and in published material.

- Mentor AI Leads and engineering teams; grow the bench of people who can do this role.

- Collaborate with hyperscalers and product partners to craft next-generation AI-led experiences and co-funded initiatives.

Must-have skills and experience :

Architecture and platform depth :

- Proven track record architecting and shipping production-grade GenAI/agentic systems in enterprise environments.

- Deep command of LLM selection and evaluation, context engineering, model routing and cost control, and the trade-offs between prompting, retrieval, tool use, and fine-tuning.

- Strong microservices and integration architecture : event-driven and API-led patterns, identity and access, secrets, multi-tenancy, and integration with core enterprise systems.

- Enterprise concerns as first-class design inputs : security, privacy, data residency, model governance, responsible AI, and regulatory constraints.

Agentic AI engineering (hands-on) :

- Hands-on working knowledge of defining and configuring prompts, instructions, tools, reasoning strategies, guardrails, memory, and orchestration in AI agent development.

- Design and debugging of multi-agent and long-horizon workflows, including failure modes, recovery, and human-in-the-loop control points.

- Strong experience with RAG pipelines, vector databases, tokenization, and prompt engineering.

Engineering craft (hands-on) :

- Hands-on experience creating and maintaining Python libraries; working command of LangChain, Hugging Face, OpenAI/Anthropic APIs, and locally hosted open models.

- Very strong experience developing RESTful APIs in Python with FastAPI and integrating third-party services, UI components, and APIs.

- Hands-on Docker-based deployment, CI/CD, and disciplined use of Git/GitHub for source control and review.

- Working knowledge of modern data platforms and their GenAI surfaces (Databricks, Snowflake Cortex, lakehouse patterns).

Model provider and hyperscaler GenAI platform know-how :

- Broad, current understanding of the GenAI offerings across the major frontier-model providers and hyperscalers, with deep, hands-on mastery of at least one.

- Able to construct and defend a multi-provider model strategy : capability-to-task mapping, model routing and tiering, benchmark and eval evidence, context-window and latency envelopes, unit economics, rate limits and capacity planning.

- Fluent in the enterprise decision criteria : data residency and sovereignty, tenant isolation, IP and training-data commitments, certifications and auditability.

- Designs for portability where it matters : provider-abstracted interfaces, gateway/routing layers, and prompt and eval assets that survive a model or vendor change.

Consultative and commercial ability :

- Demonstrated ability to turn ambiguous business problems into crisp AI use cases with clear value hypotheses and measurable outcomes.

- Excellent communication and articulation skills; credible with a CIO/CDO and with a client's principal engineer.

- Commercial fluency : estimation, deal shaping, pricing implications of architecture choices.

- Hands-on prototyping mindset.

Good to have :

- Prior experience in a pre-sales, consulting, solution-architecture, or forward-deployed engineering capacity at scale.

- Recognised depth in one or more industry domains (BFSI, healthcare/life sciences, retail/CPG, manufacturing, telecom, energy).

- Experience running partner-funded or co-innovation programmes with a hyperscaler or model provider.

- Certification on a hyperscaler AI/ML or architect track.

- Experience with self-hosted / open-weight model deployment (vLLM, Bedrock or Vertex custom endpoints, on-prem GPU).

- Published thought leadership, patents, open-source contributions, or conference speaking on GenAI/agentic systems.