AI/ML Architect

About Ciiindion

Ciiindion is a deep tech startup building mission-critical digital infrastructure: systems that must work reliably, securely, and at scale where failure is not an option. We are an AI-first company. AI is part of how we design, build, test, and operate everything, and it shapes how our small team ships far more than a conventional team could.

About the role

As an AI/ML Architect, you'll do four things. You'll architect the AI capabilities inside our products, set the practices that make our whole engineering team AI-native, build the intellectual property that gives Ciiindion a lasting edge, and represent our technical vision to customers and the academic community. You'll decide where AI creates real business value and where it only adds cost, direct AI tools to build complex systems faster, and keep overall architectural control and engineering rigor while doing it. For the right person, this role can grow into a co-founder position.

What you'll do

• Design end-to-end architecture for AI/ML platforms: data ingestion, model training and serving, orchestration, and observability.
• Lead the design of LLM-based systems, including RAG pipelines, agent frameworks, fine-tuning strategies, evaluation workflows, and guardrails.
• Optimize AI with transformer architectures: tune attention, tokenization, quantization, distillation, and inference serving to raise accuracy and cut latency and cost.
• Architect for demanding environments: high availability, low latency, edge and on-prem deployments, and secure or air-gapped models.
• Make build-vs-buy calls: use an existing AI service or API, fine-tune a model, or run open-source and local models.

Make engineering AI-native

• Define how we use AI across requirements, architecture, coding, testing, debugging, documentation, and DevOps.
• Establish workflows for AI-assisted development, including "vibe coding," where engineers move from writing every line to architecting, directing, and validating AI-generated software.
• Set guardrails against the known pitfalls: hallucinations, technical debt, security gaps, poor maintainability, and loss of context.
• Choose the right tools and models for each task (premium, smaller, or open-source/local) based on quality, cost, and data sensitivity.
• Coach engineers on using AI well, including how to give it the right context and specifications.

Build complex systems with AI, with human judgment in control

• Break large systems into components that AI can help design and build, while you retain architectural ownership.
• Use AI for system design reasoning, APIs, data models, integrations, distributed systems, code review, testing, and troubleshooting.
• Be clear about where AI accelerates delivery and where human engineering judgment is essential.

Create and protect intellectual property

• Identify and drive investments in intellectual property: novel algorithms, architectures, models, datasets, and methods that differentiate Ciiindion.
• Work with the founders to prioritize which ideas to patent, publish, keep as trade secrets, or open-source, weighing cost, competitive value, and time to market.
• Lead invention disclosures and patent filings, and build a culture where engineers routinely turn their work into protectable IP.
• Ensure IP hygiene across AI development, including the licensing of open-source models and datasets and the ownership of AI-generated code and outputs.

Engage with customers and academia

• Work directly with customers to understand their operational problems, shape solutions, and present our AI architecture and roadmap with credibility.
• Take part in technical discussions, proofs of concept, and design reviews with customer teams, and turn their feedback into product direction.
• Build relationships with leading academic institutions and research groups, through collaborations, research partnerships, internships, and campus engagement.
• Represent Ciiindion at conferences, workshops, and industry forums, and help bring emerging research into our products.

Run AI with business discipline

• Track the economics of AI: cost per task or transaction, productivity gains, and ROI.
• Define MLOps and LLMOps standards: CI/CD for models, versioning, monitoring, drift detection, and cost and performance optimization.
• Build security, privacy, and compliance in from day one.
• Mentor engineers and data scientists, run design reviews, and keep up with advances in AI research and tooling.

Who we're looking for

• Engineering graduate from a top-tier institute (B.E./B.Tech/M.Tech or equivalent in computer science, electronics, or a related field).
• 3-9 years in software engineering, ML engineering, or data/AI platforms, with growing ownership of system design and technical decisions.
• An AI-first mindset, shown by how you already work: daily, hands-on use of AI coding and design tools, with clear views on what works and what doesn't.
• Hands-on experience with ML/DL frameworks (PyTorch, TensorFlow) and modern LLM tooling (e.g., Hugging Face, LangChain/LlamaIndex, vLLM).
• Deep understanding of transformer architectures and how to optimize them for accuracy, latency, and cost, including fine-tuning, quantization, distillation, and efficient inference.
• Experience designing distributed, cloud-native systems on AWS, Azure, or GCP, including containers, Kubernetes, and streaming/batch data pipelines.
• A track record of taking AI/ML systems from prototype to production.
• Sound judgment on AI cost versus value, and the ability to explain trade-offs to engineers, executives, and customers.
• Strong grounding in model evaluation, observability, reliability engineering, and responsible AI.
• An eye for novelty: the ability to spot patentable and publishable ideas in everyday engineering work.
• High energy and drive, with the enthusiasm to move fast in a startup and lift the people around you.
• Excellent communication skills, written and verbal, with the ability to make complex AI and architecture ideas clear to customers, researchers, and engineers alike.
• Strong team skills: you collaborate openly, give and take feedback well, and make the team around you better.
• Comfort in customer-facing and academic settings, from technical demos to research discussions.
• An ownership mindset, with the ambition to build a company, not just fill a role.

Nice to have

• Publications and patents in AI/ML or related fields.
• Experience with AI in critical infrastructure such as telecom, data centers, aviation, or other regulated and safety-sensitive domains.
• Experience with edge AI, GPU infrastructure, and inference optimization.
• Familiarity with SOC 2, ISO 27001, GDPR, and secure deployment practices.
• Prior startup experience, or open-source contributions.

Why join us

• Co-founder status for a strong and suitable candidate.
• Competitive salary and an attractive equity allocation, so you share in the value you help build.
• Build an AI-first engineering culture from Day 1, with a real say in tools, workflows, and standards.
• Own the IP strategy of a deep tech company from the start.
• Work on AI problems where reliability and trust matter, with direct exposure to customers and leading academic partners.
• High ownership and direct impact, with close collaboration with the founders and a flat structure.

How to apply

Send your resume and a short note on an AI/ML system you've architected and shipped, plus how you use AI in your own engineering workflow, to chetansj@ciiindion.com