AI Solutions Architect

    Chandigarh
    10+ Years Experience

    Role Overview

    This role is designed for a practitioner who has evolved from deep experience in Artificial Intelligence into hands-on, production-grade software development using AI-assisted methodologies. The individual is expected to architect, build, and deliver robust, scalable products by leveraging AI not merely as a support tool, but as a core development paradigm.


    In addition to technical excellence, this role carries a strong leadership mandate—to institutionalize AI-driven development practices and actively elevate the capabilities of the broader engineering team.

    Key Responsibilities

    AI-Native Product Development

    • Design and deliver end-to-end software solutions using AI-assisted development workflows.
    • Translate business problems into scalable system architectures and working products.
    • Own delivery from concept → prototype → production.

    AI-Assisted Engineering Practices

    • Use advanced AI tools (LLMs, agents, code generation systems) to accelerate development while maintaining code quality and architectural integrity.
    • Establish patterns for prompt engineering, agent orchestration, and reusable AI-driven workflows.
    • Ensure generated code adheres to best practices in modularity, performance, and security.

    System Architecture & Design

    • Define backend, frontend, and data architectures for modern applications (web, SaaS, enterprise systems).
    • Design APIs, data models, and workflows optimized for AI-augmented systems.
    • Integrate AI components (NLP, CV, predictive models) into production-grade systems.

    Engineering Governance

    • Enforce code quality standards, version control discipline, testing strategies, and CI/CD pipelines.
    • Review and refine AI-generated code to meet production standards.
    • Establish guardrails for reliability, observability, and maintainability.

    Rapid Prototyping & Iteration

    • Build functional prototypes at high velocity using AI tools.
    • Iterate quickly based on stakeholder feedback and evolving requirements.
    • Balance speed with long-term scalability and technical debt management.

    AI Strategy & Enablement

    • Define how AI can be systematically leveraged across engineering workflows.
    • Evaluate and integrate emerging AI tools and frameworks into the development stack.
    • Drive adoption of AI-native development practices across teams.

    Leadership & Capability Building

    Team Enablement

    • Mentor engineers in adopting AI-assisted development workflows effectively and responsibly.
    • Conduct hands-on sessions, code walkthroughs, and live builds to demonstrate best practices.
    • Enable teams to move from ad-hoc AI usage to structured, repeatable engineering approaches.

    Upskilling & Knowledge Transfer

    • Design internal playbooks, templates, and reusable patterns for AI-driven development.
    • Create documentation and training material to standardize practices across teams.
    • Act as a multiplier—raising the overall productivity and capability of the engineering organization.

    Technical Leadership

    • Lead by example through high-quality implementations and disciplined engineering practices.
    • Influence architectural decisions and guide teams on trade-offs between speed and scalability.
    • Foster a culture of experimentation balanced with accountability and production readiness.

    Required Qualifications

    Experience

    • 10+ years in AI / Machine Learning / Data Science or related domains.
    • Recent, hands-on experience building production software using AI-assisted coding tools.
    • Demonstrated track record of delivering real-world products (not just prototypes).

    Technical Expertise

    • Strong proficiency in modern programming languages (e.g., JavaScript/TypeScript, Python, or similar).
    • Experience with backend frameworks (Node.js, Express, FastAPI, etc.) and modern frontend stacks.
    • Solid understanding of databases (SQL), APIs, and distributed systems.

    AI Engineering Capability

    • Deep familiarity with LLMs, prompt engineering, and agent-based systems.
    • Experience integrating AI models into applications (APIs, pipelines, inference systems).
    • Understanding of AI limitations, evaluation, and reliability considerations.

    Software Engineering Fundamentals

    • Strong grasp of system design, scalability, and performance optimization.
    • Experience with DevOps practices: CI/CD, containerization, cloud environments.
    • Ability to write clean, maintainable, and testable code—even when AI-generated.

    Preferred Qualifications

    • Experience building internal AI tooling, developer platforms, or automation systems.
    • Familiarity with multi-agent orchestration frameworks and workflow engines.
    • Exposure to enterprise or government-grade systems with high reliability requirements.
    • Prior experience in mentoring teams or leading engineering initiatives.

    Key Traits

    • Builder & Leader: Ships products while uplifting the team.
    • AI Fluent: Uses AI as a core engineering multiplier with discipline.
    • Teacher Mindset: Actively shares knowledge and builds team capability.
    • Systems Thinker: Understands end-to-end architecture and trade-offs.
    • Ownership Driven: Accountable for outcomes, not just outputs.

    Success Criteria

    • Deliver production-ready systems at significantly accelerated timelines using AI.
    • Establish and scale AI-assisted development practices across teams.
    • Measurably improve team productivity and engineering quality through upskilling.
    • Create a self-sustaining engineering culture that effectively leverages AI.

    Interested candidates may contact

    career@jupitice.com