Qualification:

We are looking for a highly skilled and consultative AI Architect to help clients understand, shape, and activate the potential of AI in their business. This role owns the architecture of the AI solutions we propose — across pursuits, not just within one — and is accountable for the technical credibility of what we commit to.

You will partner with sales, industry specialists, engineering, and client executives to turn priority AI use cases into architectures that are demonstrable in weeks and defensible at enterprise scale: secure, cost-aware, evaluable, and buildable by the delivery teams that inherit them.

It is deliberately a hands-on architect role. You will still open the IDE, still build the hard part of the prototype, and still debug an agent that reasons badly — while setting the patterns, guardrails, and reference architectures that everyone else builds on.

Skills Required:

AI/Gen AI/Gen AI

Role:

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 (quick wins
- platform investments).
- 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; govern their reuse so pursuits start at 40% rather than zero.
- 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 — personally on the critical path, and through the FDE pod for the rest.
- Engineer and validate AI agents for reliability, reasoning, safety, and performance; establish the evaluation and red-teaming approach that lets us state quality claims with evidence.
- 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; handle the hardest objections in the room.
- Refine solution narratives based on client feedback and market conditions.
- Oversee a seamless transition from sales to delivery — architecture decision records,



working code, known risks, and continuity of the people involved.

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 — not only pilots.
- 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 — including retrieval evaluation and knowledge-freshness strategy.

Engineering craft (hands-on)

- Hands-on experience creating and maintaining Python libraries; working command of LangChain (or equivalent orchestration frameworks), Hugging Face, OpenAI/Anthropic APIs, and locally hosted open models.
- Very strong experience developing RESTful APIs in Python with FastAPI (or similar) 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 — mandatory.
- 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 — deep enough to design on it, defend it in a client architecture review, and know where it breaks:
- Anthropic — Claude model family, Claude API, tool use and extended thinking, Claude Agent SDK, MCP (Model Context Protocol) as an integration standard, Claude delivered via Amazon Bedrock / Vertex AI, and enterprise deployment and governance patterns.




- OpenAI — GPT model family, Responses/Assistants APIs, Agents SDK, function calling, structured outputs, fine-tuning, batch and realtime APIs, enterprise data-handling commitments.
- AWS — Amazon Bedrock (multi-model access, Knowledge Bases, Guardrails, Agents/AgentCore), SageMaker, and integration with AWS data, identity and security services.
- Google — Gemini model family, Vertex AI (Model Garden, grounding, agent tooling, evaluation), and Gemini enterprise offerings.
- Microsoft Azure — Azure OpenAI / Azure AI Foundry, AI Agent Service, AI Search for retrieval, and Azure-native identity, networking and compliance patterns.

- 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, deprecation and version-migration risk.
- Fluent in the enterprise decision criteria that actually settle these choices — data residency and sovereignty, tenant isolation, IP and training-data commitments, certifications and auditability, regional availability, and the client's existing cloud and licensing commitments.
- Designs for portability where it matters: provider-abstracted interfaces, gateway/routing layers, and prompt and eval assets that survive a model or vendor change, without over-abstracting to the point of losing provider-specific capability.
- Keeps pace with a fast-moving field and translates new releases into concrete client prospect, filtering hype from what is production-ready.

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 in the same day.
- Commercial fluency: estimation, deal shaping, pricing implications of architecture choices, and the discipline to say no to a use case that will not survive contact with production.
- Hands-on prototyping mindset — able to quickly build functional demos, agent workflows, or LLM-powered interactions that accelerate client conviction.

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 (AWS, Microsoft, Google, Anthropic, OpenAI, NVIDIA or ISV partners), including funding programmes, early-access participation, and joint go-to-market.
- Certification on a hyperscaler AI/ML or architect track, or equivalent demonstrable platform depth.
- Experience with self-hosted / open-weight model deployment (vLLM, Bedrock or Vertex custom endpoints, on-prem GPU) for clients with strict data-boundary requirements.
- Published thought leadership, patents, open-source contributions, or conference speaking on GenAI/agentic systems.

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Experience:

10 to 15 years

Job Reference Number:

14142

📌 AI Architect (Indore)
🏢 Impetus Technologies
📍 Indore