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 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

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; robust equivalent capability also considered

15

Mandatory (Note 1): CTC is inclusive of the variable component (around 25:75)

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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

📌 AI Forward Deployed Lead/Architect (Enterprise Solutions) | 7-10 YOE | Onsite - Hyderabad |
🏢 MCS Chennai
📍 Hyderabad