AI Forward Deployed Lead/Architect (Enterprise Solutions) | 7-10 YOE | Onsite - Hyderabad |

Roles & Responsibilities

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 Anthrobytes 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

• 1
• Strong AI Forward Deployed Lead/Architect Profile owning the full journey from client discovery to production, hands-on and client-facing
• 2
• Mandatory (Experience 1): Must have 7+ years of experience, with hands-on production AI/ML work (LLMs, NLP, computer vision or advanced analytics)
• 3

Mandatory (Experience 2): 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

4

Mandatory (Experience 3): 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)

5

Mandatory (Experience 4): 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

6

Mandatory (Experience 5): 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

• 7
• Mandatory (Experience 6): Must have worked directly with enterprise-level clients in a client-facing capacity
• 8
• Mandatory (Experience 7): Must have built and led their own team, creating a pod structure per project, and mentored junior AI engineers and consultants
• 9
• Mandatory (Tech skill 1): Must be hands-on with Python, LLM frameworks (LangChain/LlamaIndex/HuggingFace) and cloud AI (AWS/Azure/GCP)
• 10

Mandatory (Skill 1): 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

11

Mandatory (Skill 2): Must take strong ownership, be genuinely enthusiastic and client-facing (not a pure-research or low-energy profile), thrive in ambiguity and 0 to 1 environments, and want to join long-term as part of the company (ESOP pool)

• 12
• Mandatory (Company): Must have experience across both services and product (this is a services-plus-product hybrid; candidates keen on pure product only are not a fit)
• 13

Mandatory (Background): Must come from an AI/ML, data analytics or data-platform company with enterprise client delivery, ideally with startup or growing-company experience having seen AI build cycles in 0 to 1 / ambiguous environments; Supply Chain AI exposure, or manufacturing/logistics, B2B SaaS AI or MLOps background, a plus

• 14
• Mandatory (Pedigree): Must have strong pedigree, ideally IIT, then BITS/NITs/IIITs, with a CSE/IT background; strong equivalent capability also considered
• 15
• Mandatory (Note 1): CTC is inclusive of the variable component (around 25:75)
• 16
• Mandatory (Note 2): Must be open to starting on a consultant basis first, then converting to a full-time role
• 17
• Preferred (Global exposure): Global client exposure (US/Europe/Middle East/SEA) and cross-cultural fluency