Job Description

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.
1–3 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