- Lead the pre-sales and technical discovery sessions with enterprise and mid-market clients globally - understand their pain points, data maturity, and AI readiness before anyone writes a line of code.

- Own the solution design for AI proposals : use case scoping, high-level system design, data requirements, and ROI framing.

- Build and present compelling PoC demos, solution blueprints, and technical proposals in partnership with the commercial team.

- Respond to RFPs and RFIs with technically rigorous and commercially sharp responses that win confidence.

- Act as a trusted AI advisor to client stakeholders - CXOs, VPs of Operations, IT leaders - not just a technical vendor on the call.

- Run workshops, AI readiness assessments, and value stream mapping sessions at client sites and remotely.

- Navigate ambiguous problem spaces and help clients articulate what they actually want to solve with AI, and why it matters.

- Build long-term client relationships that translate into deepened engagements, referrals, and expanded mandates.

- Be deeply involved in deployment - not just design. Understand production constraints, integration complexity, and change management realities.

- Collaborate with engineering and data science teams to ensure solution designs are buildable and delivered to the quality clients expect.

- Drive go-live milestones, client acceptance, and post-deployment success metrics with accountability.

- Debug, iterate, and adapt: you own outcomes, not just deliverables. If something is broken, you fix it.

- Define AI architecture patterns for the firm : LLM orchestration, RAG pipelines, agentic workflows, multi-modal systems.

- Stay ahead on applied AI research and emerging frameworks; bring relevant signals back to the team before the market does.

- Mentor junior AI engineers and consultants on both client engagement craft and solution architecture rigour.

- Contribute to Anthrobyte's knowledge capital - case studies, whitepapers, internal playbooks, and reusable solution accelerators.

- Develop the whole tech department.

- Create pods kind of a structure for each and every project.

Ideal Candidate :

- Strong AI Forward Deployed Lead/Architect Profile owning the full journey from client discovery to production, hands-on and client-facing.

- Must have 7+ years of experience, with hands-on production AI/ML work (LLMs, NLP, computer vision or advanced analytics).

- Must be deeply hands-on, able to build SLMs, agentic structures, RAG pipelines, LLM orchestration and multi-modal systems, and get into cost/credit optimisation detail, not just high-level oversight.

- Must have 4+ years client-facing as a trusted AI advisor to CXOs/VPs of Operations/IT leaders, with a proven presales/solution-consulting track record (technical discovery, use-case scoping, solution design, ROI framing, PoC demos, blueprints, RFP/RFI responses).

- Must have run workshops, AI readiness assessments and value-stream-mapping sessions, navigated ambiguous problem spaces, and built long-term client relationships that lead to referrals and expanded mandates.

- Must have designed and deployed end-to-end AI systems (ingestion, pipelines, deployment, monitoring, iteration), owning production constraints, integration, change management, go-live, client acceptance and post-deployment success, owning outcomes and fixing what breaks.

- Must have worked directly with enterprise-level clients in a client-facing capacity.

- Must have built and led their own team, creating a pod structure per project, and mentored junior AI engineers and consultants.

- Must be hands-on with Python, LLM frameworks (LangChain/LlamaIndex/HuggingFace) and cloud AI (AWS/Azure/GCP).

- Must have strong storytelling, making complex AI accessible to business audiences without dumbing it down, and define AI architecture patterns and bring emerging frameworks/research back to the team.

- Must take strong ownership, be genuinely enthusiastic and client-facing (not a pure-re).