Senior AI Engineer

About Us: Founded in 1972, Atari is one of the world’s most iconic consumer brands and a pioneer in the video game industry, known for creating classics like Pong, Asteroids, and Centipede. Today, Atari Inc. continues to build on its legacy by developing games, hardware, and experiences that honor the past while driving innovation for the future. 

Over the past two years, we've been building Atari India, a growing team that plays a critical role in supporting our global operations. We're proud of the team we've assembled so far, and we’re just getting started. As part of a lean, high-impact organization, the team in India works closely with colleagues in North America and Europe on projects that move the company forward. Whether you're helping launch a new game, keeping our infrastructure secure, or supporting day-to-day operations, your work here matters. Join us as we continue to grow Atari India and build the future of a legendary brand.

Position: Senior AI Engineer
Experience: 5+ Years
Location: Netaji Subhash Place, Pitampura, Delhi.
Employment Type: Full-Time
Working Hours: 9:00 AM - 6:00 PM (IST)

About the Role
We are seeking an exceptional Full Stack Developer (AI Applications) to build and scale the next generation of AI-driven products at Atari. This role requires deep expertise across the entire application stack, with a strong focus on integrating and optimizing Generative AI and Machine Learning models as core product components. The ideal candidate will bridge the gap between data science and product engineering — transforming data models into intelligent, user-facing features that enhance product performance, usability, and value.

Responsibilities

A. Full Stack Application Development

•  Architecture & Design: Design and implement scalable, high-performance architectures for front-end interfaces (React, Vue, or Angular) and back-end microservices (Python/Node.js).
• API Development: Build, document, and secure efficient RESTful or GraphQL APIs to enable seamless data and model communication across systems.
• Data Persistence: Configure and optimize data models in relational (PostgreSQL, MySQL) and non-relational Vector Databases to support AI workloads.
• Testing & CI/CD: Develop unit, integration, and end-to-end tests; manage deployment pipelines ensuring quality, stability, and reliability.

B. AI Product Integration and MLOps Focus

• Model Implementation: Integrate Large Language Models (LLMs) and machine learning artifacts into production environments with an emphasis on latency, reliability, and cost efficiency.
• RAG System Engineering: Lead the design and development of Retrieval-Augmented Generation (RAG) systems — managing data chunking, embedding, indexing, and retrieval for contextual responses.
• Performance Optimization: Improve AI application responsiveness via prompt engineering, caching, and inference optimization.
• Cross-Functional Collaboration: Work closely with Data Scientists and MLOps Engineers to deploy, monitor, and continuously improve AI systems in production.

Requirements

• 5+ years of experience as a Full Stack Software Engineer, including at least 1 year of hands-on AI/ML integration experience.
• Strong front-end development skills in TypeScript/JavaScript and frameworks like React or Next.js.
• Advanced back-end development experience in Python (preferred) or Node.js, with proven ability to build scalable APIs.
• Proficiency with LLM APIs (OpenAI, Gemini, Claude), frameworks like LangChain or LlamaIndex, and Hands-on experience with Claude Code and Model Context Protocol (MCP).
• Experience with containerization (Docker) and deployment on major cloud platforms (AWS, GCP).

Bonus Points

• Hands-on experience building or maintaining RAG pipelines
• Familiarity with Claude Code and Model Context Protocol (MCP).
• Exposure to vector databases (Pinecone, Weaviate, Chroma).
• Familiarity with CI/CD automation for ML-integrated applications.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related technical field.
• Strong understanding of MLOps principles and AI model lifecycle management.
• Familiarity with cloud-based orchestration (Kubernetes) and infrastructure-as-code concepts.