Role Overview

We are looking for a hands-on Engineering Lead / Solution Architect to lead the design, development, and production readiness of AI-powered and SaaS applications. The role combines solution architecture, technical decision-making, hands-on coding, code quality ownership, and developer mentoring. This is a technical leadership role rather than a project management position.

Key Responsibilities

Solution Architecture & Technical Design

- Design scalable, secure, maintainable SaaS and AI application architectures.
- Translate business requirements into technical designs and implementation plans.
- Define frontend, backend, API, database, and integration architecture; select appropriate technologies and infrastructure.
- Create architecture diagrams, technical specifications, and Architecture Decision Records (ADRs).
- Assess feasibility, scalability, performance, security, and technical risks before development begins.

Technical Leadership & Code Quality

- Lead and mentor a team of approximately 67 software developers, including junior engineers.
- Review code and pull requests; establish coding standards, Git workflows, and branch protection practices.
- Write production-quality code for complex or critical modules and resolve challenging technical issues.
- Identify design flaws, security vulnerabilities, performance issues, and technical debt.
- Guide developers on debugging, refactoring, testing, and maintainable software design.

AI-Assisted Development & AI Architecture

- Define standards for responsible AI-assisted coding using tools such as GitHub Copilot, Cursor, Claude Code, or similar tools.
- Review AI-generated code for correctness, security, maintainability, and performance.
- Establish secure practices for handling confidential information, source code, and credentials when using AI tools.
- Design and review AI-powered application architectures, including Retrieval-Augmented Generation (RAG) solutions.
- Guide document ingestion, embeddings, vector search, retrieval pipelines, and LLM API integrations.
- Evaluate AI response quality, latency, security, and operating costs; support integrations with ML or computer vision systems when required.

DevOps, Security & Production Readiness

- Define CI/CD pipelines, deployment standards, and development, staging, and production environments.
- Work with cloud platforms such as AWS, Azure,



or DigitalOcean and container technologies such as Docker.
- Establish secure configuration and secrets management, logging, monitoring, alerting, and error tracking.
- Review authentication, authorization, database migrations, backups, disaster recovery, and rollback procedures.
- Support production incidents, root-cause analysis, and release-readiness reviews.

Quality Assurance, Mentoring & Delivery

- Define unit, integration, API, and end-to-end testing standards and promote automated test coverage.
- Work with QA to define release acceptance criteria and prevent releases with critical unresolved defects.
- Mentor developers through code reviews, knowledge-sharing sessions, and structured feedback.
- Break complex features into manageable technical tasks, estimate effort, and identify dependencies.
- Coordinate technical work across products and report progress, risks, and engineering quality metrics.

Required Technical Skills

- Backend development with Python, Node.js, TypeScript, or equivalent technologies.
- REST API design, authentication, authorization, API security, and microservices or modular-monolith architecture.
- Working knowledge of React, Next.js, or similar frontend frameworks.
- Relational databases such as PostgreSQL, including schema design, indexing, migrations, and query optimization.
- Understanding of vector databases, caching, LLM APIs, RAG, embeddings, and retrieval pipelines.
- Docker, CI/CD tools such as GitHub Actions, Linux administration, cloud infrastructure, monitoring, and incident management.
- Git and pull-request workflows, SOLID principles, design patterns, automated testing, secure coding, and OWASP principles.
- Technical documentation, architecture diagrams, and technical decision records.

Required Qualifications

- Bachelor’s degree in Computer Science, Information Technology, Engineering, or equivalent practical experience.
- 8–12 years of software engineering experience,



including at least 3 years in technical leadership or solution architecture.
- Demonstrated experience delivering and supporting production applications.
- Strong hands-on coding, debugging, system design, API architecture, and database design skills.
- Experience leading and mentoring developers, with practical knowledge of cloud deployment, CI/CD, and application security.
- Strong technical communication and documentation skills.

Preferred Qualifications

- Experience building SaaS products from initial development through production.
- Experience with multi-tenant applications and AI-powered products using RAG or LLM integrations.
- Familiarity with Supabase, PostgreSQL, Docker, DigitalOcean, Coolify, or similar deployment platforms.
- Experience governing AI-assisted development teams and improving observability, security testing, and performance.
- Experience building reusable shared services or platforms used by multiple products.

Expected Outcomes

- Approved architecture diagrams, technical specifications, and engineering standards.
- Consistent code review practices, improved code quality, and controlled technical debt.
- Reliable build, test, deployment, and production-release processes.
- Clear technical task breakdowns, estimates, and engineering status reporting.
- Structured developer mentoring and measurable improvement in team capability.
- A practical technical improvement roadmap and production-readiness plan.

First 90 Days

- Days 1–30: Assess existing architecture, repositories, developer capabilities, engineering practices, and critical risks; establish coding and review standards.
- Days 31–60: Improve architecture consistency, testing, CI/CD, release controls, and developer mentoring; address high-priority technical risks.
- Days 61–90: Establish a repeatable release process, validate production readiness for a priority product, report engineering metrics, and present a six-month improvement roadmap.

Success Measures

- Reduction in recurring defects, security findings, and maintainability issues.
- Improved production stability, incident recovery, delivery predictability, and review effectiveness.
- Increased automated test coverage and developer independence.
- Consistent architecture standards, reduced high-risk technical debt, and controlled infrastructure/AI service costs.

📌 Engineering Lead / Solution Architect (Pune)
🏢 Techknomatic
📍 Pune