AI Architect

Harbinger Techventures; Pune District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Harbinger Techventures is seeking an AI Architect for Pune to lead design of AI-assisted and AI-native solutions across digital platforms. The role will translate complex problems into clear architectures, prototypes, and credible estimates while owning risk, security, and governance for AI in content-heavy products.

You will drive solutioning, build POCs, mentor teams, and shape reusable accelerators. Deep hands-on knowledge of ML platforms and responsible AI is required for this senior

Qualifications

• 12+ years in software engineering and architecture.
• 4+ years delivering GenAI/LLM in production.
• Strong cloud experience (AWS, Azure, or GCP).
• Experience with RAG, vector search, and orchestration frameworks.

Responsibilities

• Translate ambiguous business problems and RFPs into target architectures and reference designs for AI-enabled publishing and learning products.
• Stand up proofs of concept and reference implementations that de-risk delivery.
• Establish reusable accelerators and an AI engineering playbook across teams.
• You’ll design systems where LLMs, retrieval, and agents are first-class components, spanning orchestration, grounding, evaluation, and observability.

Skills

LLM/GenAI design

Cloud platforms

RAG & vector search

MLOps/LLMOps

Security by design

Prototyping & estimation

Stakeholder communication

Tools

MLflow

Kubeflow

Weights & Biases

SageMaker

Azure ML

Vertex AI

Job description

Designation: AI Architect Digital Publishing

Office Location: Pune

Role overview

We're looking for an AI Architect having more than 12 years of experience to lead the design of AI-assisted and AI-native solutions across our digital platforms. Architect will turn open-ended problems into clear architectures, working prototypes, and credible estimates quickly while owning the risk, security, and governance that come with putting AI into content- and IP-heavy products. It's a hands-on, high-influence role that sits between engineering, product, and client-facing teams.

Responsibilities

Solutioning architecture

• Translate ambiguous business problems and RFPs into target architectures, trade-off analyses, and reference designs for AI-enabled publishing and learning products.
• Pick the right pattern for each problem prompt/workflow, RAG, fine-tuning, agents/MCP, or classic ML and justify build-vs-buy and model choices.
• Rapid estimation enablement
• Produce fast, defensible effort estimates, delivery approaches, and team shapes for proposals and new initiatives.
• Support pre-sales and client conversations with architecture walkthroughs pitched anywhere from engineer to C-suite.

Hands-on AI build POCs

• Stand up proofs of concept and reference implementations that de-risk the hard parts before full delivery commits.
• Define evaluation harnesses, guardrails, and acceptance criteria so AI features ship with measurable quality.

Standards, reuse mentoring

• Establish reusable accelerators, patterns, and an AI engineering playbook across teams.
• Mentor engineers on AI-assisted development and raise the bar on quality, security, and cost awareness.

AI-native AI-assisted development

We mean two different things here, and we expect both.

AI-assisted development you use AI across the SDLC and lift team velocity with it. You're fluent with AI coding assistants and agentic developer workflows, you prompt and review their output critically, and you know where they genuinely help versus where they create rework or risk.

AI-native development you design systems where LLMs, retrieval, and agents are first-class components, not bolt-ons. That spans orchestration and tool use (including MCP), retrieval and grounding, evaluation and observability, guardrails, prompt and context management, and the cost and latency engineering these systems need to survive production.

Technology stack

You’ll architect and build across our core stack, so hands-on fluency in most of these is expected:

• Backend: .NET, Python/Java
• Frontend full-stack: TypeScript/JavaScript and modern full-stack development
• Cloud: Azure-based cloud development and services, Cloud Native development
• Deep experience with cloud ecosystems like Amazon Web Services (AWS) (Amazon SageMaker), Microsoft Azure (Azure ML), or Google Cloud Platform (GCP) (Google Vertex AI).
• Familiarity with MLOps and orchestration tools such as MLflow, Kubeflow, or Weights Biases.

Responsible AI

You treat AI as a capability to be governed, not just adopted.

Risk mitigation you can name and design around the failure modes of AI usage: hallucination and grounding failure, IP and content leakage, data governance and privacy, evaluation gaps, runaway cost and latency, and model or vendor lock-in. You build in human-in-the-loop checkpoints, evals, and fallbacks rather than trusting the model to behave.

Security you understand the security surface that AI and agents introduce, and how it differs from traditional app security: prompt injection (direct and indirect), retrieval that authorises before it retrieves rather than after, agent and tool permissioning, MCP scopes and confused-deputy risks, tenant isolation, and audit trails of what the model saw, decided, and did.

In a publishing context you also think about content and IP exposure what a legitimate account, automated user, or agent can extract from the platform, and how that extraction would be detected and traced.

What you bring

• 12+ years in software engineering and architecture, including 4+ years designing or delivering LLM/GenAI solutions in production (tune the years to your seniority bar).
• Strong grounding in cloud (AWS, Azure, or GCP), modern application and data architecture, and API/integration design.
• Practical depth in several of: RAG and vector search, orchestration/agent frameworks, prompt and context engineering, evaluation tooling, and MLOps/LLMOps.
• Security fundamentals for web and AI systems (authentication/authorization, data protection, secure SDLC) plus a working command of AI-specific threats.
• A track record of estimating, scoping, and sequencing delivery, and of communicating clearly with both engineers and executives.
• Comfortable being hands-on in code and prototypes, not only on slides.
• Digital publishing or eLearning domain familiarity is a strong plus: content platforms and headless CMS, SCORM/xAPI, DRM and content protection, metadata, rights, and accessibility.

Bonus points

• Experience with MCP, multi-agent systems, or agent evaluation and safety tooling.
• Publishing and learning standards: EPUB, ONIX, SCORM/xAPI, QTI, IMS/LTI.
• Relevant cloud or AI certifications, and recognised security credentials.
• Open-source contributions, writing, or speaking on applied AI or AI security.