AI Engineer

ProductSquads; Ahmedabad District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

ProductSquads seeks engineers to design, build and operate AI-enabled software where AI is core, not add-on. You will create agentic systems that autonomously select tools, manage context, and drive multi-step tasks toward goals.

Join us to translate requirements into scalable, secure, and observable AI solutions, integrating AI with real-world services and APIs while collaborating with Product and engineering teams.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field or equivalent practical experience.
• Strong software engineering fundamentals and production systems experience.
• Hands-on with agentic AI, allowing models to select tools and perform multi-step tasks.
• Experience using MCPs or equivalent to manage tools, context, memory.
• Technical understanding of AI models including versions, context windows and trade-offs.

Responsibilities

• Design, build, and operate production-grade software systems that embed AI as a core capability.
• Develop agentic systems that autonomously select tools, manage context, and execute tasks.
• Implement MCPs or equivalent mechanisms to manage context, tools, memory, and boundaries.
• Translate requirements into scalable, secure, observable AI-enabled implementations.
• Collaborate with Product, Scrum Masters, and engineers to deliver AI-powered features end to end.

Skills

Strong software engineering

Agentic AI systems

Model Context Protocols

AI models experience

Open-source AI models

APIs integration

Autonomous teams

Education

Bachelor's degree in CS/Engineering

Tools

Open-source AI tools

Cloud-native stacks

Job description

• Design, build, and operate production-grade software systems that embed AI as a core capability rather than an add-on.
• Build and integrate agentic systems that can autonomously select tools, manage context, and execute multi-step tasks toward defined goals.
• Implement Model Context Protocols (MCPs) or equivalent mechanisms to manage context, tools, memory, and system boundaries in production environments.
• Translate product requirements into AI-enabled implementations that are scalable, reliable, secure, and observable.
• Apply AI through prompting, orchestration, model composition, evaluation, and deployment, focusing on runtime behavior rather than training.
• Treat model inputs, outputs, and behavior as versioned, testable components of the system.

AI-First Software Development

• Use AI directly in daily engineering work, including:
• AI-assisted and AI-generated code
• Autonomous and semi-autonomous engineering agents
• AI-driven test generation, debugging, and refactoring
• AI-enabled documentation and observability
• Treat AI as a default everyday tool to increase personal and team leverage.
• Iterate on prompts, policies, agent flows, and contextual inputs as part of normal development workflows to improve system behavior and outcomes.
• Apply and refine AI-enabled development practices within assigned systems and team workflows to reduce manual effort and improve delivery quality.
• Partner closely with Product, Scrum Masters, and other engineers to deliver AI-powered features end to end. Take leadership of these roles at times, including acting as a single person team when appropriate for the project. This is true full-stack in an AI-powered environment.
• Integrate AI systems with real-world services, APIs, and data sources as part of production applications.
• Contribute to shared platforms, services, and internal tooling by implementing AI enabled solutions aligned with established patterns and architectural direction.
• Balance rapid iteration with sound engineering judgment, system boundaries, and long term maintainability.

Quality, Reliability & Accountability

• Own the quality of your deliverables top to bottom of the stack. Partner with QA team members when available, but build AI agents focused on guaranteeing quality of what you build without the need of others.
• Ensure AI-enabled systems meet enterprise standards for security, performance, reliability, and compliance.
• Implement safeguards, monitoring, and evaluation mechanisms for AI behavior in production, including detection of hallucinations, regressions, and unintended behavior.
• Treat evaluation, traceability, and explainability as ongoing engineering concerns rather than one-time validation steps.
• Take ownership of assigned solutions from initial design through deployment and ongoing operation.
• Stay current on AI tools, platforms, models, and agentic patterns relevant to real-world software systems.
• Experiment responsibly with new AI capabilities and translate learnings into concrete improvements in the systems and workflows you own.
• Continuously improve how AI is applied within your area of responsibility through disciplined execution and iterative refinement.
• Contribute to a culture of technical excellence, accountability, and continuous learning through high-quality delivery.
• Other duties as assigned.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• Strong software engineering fundamentals, with experience building and operating production systems.
• Hands-on experience building or working with real agentic AI systems, including giving models autonomy to select tools and execute multi-step tasks.
• Experience using Model Context Protocols (MCPs) or equivalent approaches to manage tools, context, and memory in production AI systems.
• Demonstrated technical understanding of AI models, including:
• Model names and versions
• Parameter sizes and context window limits
• Trade-offs between models (latency, cost, reasoning quality)
• Experience working with open-source AI models and ecosystems, in addition to any commercial or hosted models.
• Experience integrating AI systems with real-world services, APIs, and data sources.
• Demonstrated ability to evaluate AI systems beyond accuracy, including reliability, hallucination risk, traceability, and safety considerations.
• Experience addressing scaling concerns such as latency, cost optimization, caching, parallelization, and trade-offs between retrieval-based approaches and fine-tuning.
• Demonstrated ability to move AI systems from prototype to pilot to production, including awareness of common failure modes and how to mitigate them.
• Comfort working autonomously in small, highly empowered teams.

Preferred Skills

• Experience integrating frontier and open-source models into SaaS or enterprise applications.
• Familiarity with cloud-native architectures and modern development stacks.
• Experience working in regulated, enterprise, or mission-critical environments.
• Background in product-oriented engineering organizations.