Senior AI Architecture Leader | AI-Native Enterprise Platform

Reports to: CTO / Founding Team | Focus: Production AI platform | Employment: Full-time leadership role

Role Overview:

The company is a technology firm based in Jaipur, India, building a next-generation, AI-native enterprise platform for the global maritime and shipping industry. Its first product is being developed for a leading Singapore-based shipping company, with stakeholders across Singapore, India, Europe, and the United States.

We are looking for a senior, hands-on Head of AI Platform & Architecture to work with the CTO and founding team. This person will own the shared AI intelligence layer: design it, prove it in production, and build the specialist team. This is not an AI strategy, chatbot or prompt-engineering role.

Ideal Candidate Snapshot:

Best-fit candidates have built production AI platforms at enterprise scale and can explain failures, trade-offs, governance and improvements.

Core Mission:

- Create the shared AI foundation used by all enterprise modules, instead of isolated AI solutions by product team.
- Build enterprise RAG, model orchestration/routing, agents, evaluations, observability, governance, security and AI cost controls.
- Use AI to retrieve, reason and recommend while keeping authorization, business rules and irreversible actions under enterprise controls.
- Define the cloud-versus-edge AI strategy for vessels, balancing accuracy, hardware, latency, connectivity, security and operating cost.

Key Roles and Responsibilities:

Platform Architecture:

- AI platform architecture: Own the AI gateway, multi-model strategy, RAG, hybrid search, context management, agent orchestration, evaluations, observability and cost controls.




- Enterprise knowledge and secure retrieval: Design permission-aware retrieval across manuals, procedures, work orders, maintenance history, operational records and governed enterprise data.

Safety, Governance & Quality:

- Agents, safety and oversight: Define controlled tool/API access, approval patterns, guardrails, auditability, and protected failure/abstention patterns.
- AI quality and economics: Set test datasets, groundedness metrics, regression gates, model comparisons, latency targets, token budgets and cost monitoring.
- AI-native product delivery: Partner with platform, backend and data teams so APIs, events, identity, permissions and knowledge are AI-consumable from Day 1.

Delivery & Leadership:

- Team and engineering leadership: Stay hands-on early; recruit and mentor AI/LLM, AI platform and data/knowledge engineers; support responsible AI-assisted engineering practices.

Required Candidate Profile:

Must-Have Experience:

- 10 years in software or platform engineering, with recent ownership of production AI/ML/GenAI systems.
- Hands-on architecture and delivery of enterprise LLM/GenAI platforms used by real users or business workflows.
- Production RAG and knowledge systems with ingestion, metadata, retrieval quality, authorization-aware access and evaluation.




- Agentic AI or controlled tool/API execution with authorization boundaries, failure handling and human approval.
- Model selection and routing judgment across quality, latency, privacy, availability and inference cost.
- AI evaluation and observability, including test sets, accuracy checks, regression testing, safety monitoring and feedback loops.
- Cloud-native architecture, APIs, distributed systems, enterprise security and partnership with platform/data teams.

Strongly Preferred:

- Reusable AI platform experience across multiple products or business domains.
- Enterprise SaaS/ERP, regulated or safety-sensitive systems, multi-tenant, IoT/edge or intermittently connected environments.
- Experience with major AI providers or open-source models; architecture judgment matters more than specific provider familiarity.
- Practical exposure to local/edge model deployment and related hardware and operations trade-offs.
- Zero-to-one product/platform experience in a founding, startup or transformation environment.

Candidate Profile Summary:

Target senior technology leaders who have built, operated and improved production AI systems not advisory-only or proof-of-concept profiles.

Agency Screening Guidance:

- Prioritize candidates who can say: I built this, ran it in production, saw where it failed, and changed the architecture.

Strong Fit:

- Principal/Staff AI architects, Heads of AI Platform, senior AI platform engineers, or engineering leaders with recent hands-on production ownership.

Less Suitable:

- AI strategy consultants, prompt engineers, chatbot-only developers, research-only profiles, or managers who cannot defend architecture and production failure modes.

📌 Head - AI Platform & Architecture (India)
🏢 MAXIC ARROW
📍 India