Full Stack Engineer

Title: Full Stack AI Engineer Product-minded, aligned for AI Implementations

Location: Gurgaon

Company Overview

We are a high-growth technology company building advanced software platforms for large-scale, real-time processing environments. Our products power mission-critical workloads across domains such as intelligent automation, GenAI, machine learning, and data-driven applications.

We focus on building scalable systems, solving complex engineering problems, and developing production-grade platforms that integrate modern AI capabilities with robust software engineering.

Position Overview

We are seeking a product-focused Full Stack Engineer with strong foundations in computer science and experience building scalable backend systems and modern web applications.

This role involves designing and developing end-to-end systems that integrate APIs, data pipelines, and AI/ML capabilities into production environments. The ideal candidate is comfortable working across the stack from backend services and system optimization to user-facing applications while collaborating closely with product, data science, and infrastructure teams.

Experience building AI-native applications or integrating LLM-powered systems into production software is highly valued.

Key Responsibilities

1. Full Stack Development

• Design and build scalable backend services and APIs to support high-throughput applications.
• Develop modern web interfaces using frameworks such as React.js or similar technologies.
• Use UI tools like Streamlit/Gradio to ensure rapid POCs/ demonstrations and Product Prototype implementation
• Build internal tools and dashboards for experimentation, monitoring, and product workflows.
• Implement clean, maintainable, and well-tested code across the application stack.

2. Backend Systems & Architecture

• Design distributed systems capable of handling large-scale data processing and real-time workloads.
• Optimize backend services for performance, reliability, and scalability.
• Implement API services using frameworks such as FastAPI, Flask, or gRPC.
• Work with asynchronous processing, task queues, and event-driven architectures.
• Design and manage database architectures for scalable applications.

3. Machine Learning & Data Integration

• Integrate machine learning models and data-driven services into production systems.
• Build and maintain data ingestion, preprocessing, and feature pipelines.
• Deploy and monitor ML models in production environments.
• Collaborate with data scientists to productionize experimental models.
• Manage large datasets across structured, unstructured, and multimodal sources.

4. AI & Agentic Systems Integration

• Design and integrate AI-powered workflows and agent-based systems into product applications.
• Build and manage tool integrations and APIs used by AI agents to interact with external services, databases, and internal systems.
• Implement memory management strategies such as context management, vector databases, and retrieval pipelines for agent-driven applications.
• Design mechanisms for state management, session handling, and persistence in agent-based applications.
• Build and manage knowledge pipelines and data sources used by AI systems including document stores, embeddings, and retrieval systems.
• Implement systems for monitoring, evaluation, and debugging of agent behaviour and AI-driven workflows.
• Ensure reliability through guardrails, prompt orchestration, validation mechanisms, and fallback logic in AI-powered features.

5. System Performance & Optimization

• Improve system performance through profiling, resource optimization, and efficient architecture.
• Optimize compute usage across CPUs/GPUs for ML workloads where applicable.
• Ensure applications maintain low latency and high throughput under production workloads.
• Implement caching, parallel processing, and scalable system design.

6. Security & Reliability

• Implement secure APIs and data handling practices.
• Design systems with reliability, monitoring, and observability in mind.
• Ensure compliance with data privacy and security best practices.
• Build robust logging, monitoring, and alerting systems for production environments.

7. Collaboration & Engineering Culture

• Work closely with product, infrastructure, and data teams to deliver production-ready solutions.
• Participate in architecture discussions, code reviews, and technical design.
• Contribute to engineering best practices and mentor junior engineers where applicable.

8. Continuous Learning & Innovation

• Stay updated with advancements in software engineering, distributed systems, and applied AI technologies.
• Evaluate new tools and frameworks that improve engineering productivity and system performance.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 13 years of experience in software engineering, backend development, or full-stack development.
• Strong fundamentals in data structures, algorithms, operating systems, and computer architecture.
• Proficiency in Python or similar backend programming languages.
• Experience building APIs using frameworks such as FastAPI, Flask, or similar.
• Experience with modern frontend frameworks such as React.js.
• Familiarity with distributed systems, message queues, or data processing frameworks.
• Strong debugging and problem-solving skills in complex software systems.

Preferred Skills

• Experience integrating machine learning models or AI services into applications.
• Familiarity with LLM APIs or AI orchestration frameworks such as LangChain, LangGraph, or similar tools.
• Experience working with vector databases and retrieval systems for AI applications.
• Experience with containerization and orchestration (Docker, Kubernetes).
• Familiarity with MLOps or data workflow tools (Airflow, MLflow, Kubeflow).
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience building AI-native applications or agent-based workflows.

What We Look For

• Strong engineering fundamentals and system thinking.
• Product ownership mindset.
• Curiosity for solving real-world technical problems.
• Ability to learn new technologies quickly.
• Passion for building scalable, production-grade systems.