AI Engineer

Keywords Studios is a leading global provider of technical and creative services to the video game and entertainment industries. With a network of over 70 studios across 26 countries, we partner with the world's most renowned game developers and publishers to deliver end-to-end solutions. Our expertise spans game development, art production, localization, audio services, functional and linguistic testing, and much more. At Keywords, we foster a culture of collaboration, innovation, and excellence, empowering our teams to create world-class gaming experiences that captivate audiences worldwide. In India, the company is headquartered in Gurgaon and has facilities in Bangalore and Pune. Join us in shaping the future of gaming and entertainment through technology!

Role Overview

We are looking for a passionate and skilled AI Engineer to build next-generation applications. You will play a key role in developing, deploying, and optimizing state-of-the-art Al models to power intelligent solutions that impact millions of users. This role heavily focuses on generative AI, Large Language Models (LLMs), and integrating sophisticated agentic workflows into production environments.

Benefits
What We Offer
• Competitive salary and performance bonuses.
• Opportunity to work on exciting, cutting-edge projects.
• Flexible working hours and a collaborative environment.
• Professional growth opportunities through training and mentorship.
• Health insurance, Learning budgets, etc.
• Disconnect Week during Christmas to new year.

Experience & Requirements
Key Responsibilities
• Design, develop, and deploy advanced AI solutions focused on generative capabilities and conversational Al.
• Build context-aware, autonomous AI agents using modern frameworks to solve complex business logic and automate workflows.
• Collaborate with data engineers to preprocess and curate large datasets for training and testing models.
• Experiment with state-of-the-art machine learning algorithms and fine-tune foundation models for production.
• Build and deploy scalable, production-ready AI pipelines.
• Analyze and optimize the performance of AI models to meet business requirements.
• Stay updated with the latest advancements in GenAI, LLMs, and agentic systems, and propose innovative solutions to enhance products.

Required Skills and Qualifications

Education: Bachelor's or Master's degree or PhD. in Computer Science, Data Science, or a related field.

Experience: 3 to 6 years of professional experience in building ML/AI models with a focus on modern LLM implementations.

• Technical Skills:
• Strong proficiency in Python and experience with advanced AI/LLM ecosystem libraries (e.g., transformers, vLLM, accelerate, PEFT/LoRA, or DSPy).
• Experience in building and deploying RESTful APIs using FastAPI or Flask to serve AI models in production.
• Strong expertise in building applications using LLMs (e.g., Gemini, GPT-4, Claude) and modern orchestration frameworks like LangChain or LlamaIndex.
• Hands-on experience with modern developer tools, including the Google AI SDK and OpenAI SDK, N8N.
• Deep understanding of AI agentic frameworks (e.g., LangGraph, AutoGen, CrewAI) and how to orchestrate multi-agent systems.
• Familiarity with vector databases, Retrieval-Augmented Generation (RAG) pipelines, and modern backend-as-a-service platforms like Supabase.
• Knowledge of the Model Context Protocol (MCP) to securely connect AI models with external data sources and tools.
• Experience in developing and fine-tuning deep learning models using TensorFlow or PyTorch.
• Knowledge of cloud platforms (AWS, Azure, or GCP) for deploying ML models.
• Soft Skills:
• Strong problem-solving abilities and analytical mindset.
• Excellent communication skills to convey complex ideas effectively.
• Ability to work collaboratively in a team-oriented environment.

Preferred Qualifications
• Knowledge about advanced prompt engineering techniques (e.g., Few-Shot, Chain-of-Thought, ReAct) is a strong good-to-have.
• Experience with fine-tuning large language models (LLMs) for domain-specific tasks.
• Exposure to multimodal learning (integrating vision and language models).
• Knowledge of LLMOps/MLOps practices, including containerization and CI/CD pipelines.
• Publications or contributions to open-source projects in modern AI domains.