Artificial Intelligence Engineer/Research Engineer

About Trinka AI

Trinka AI (www.trinka.ai) is an AI-powered writing assistant designed for academic and technical writing. Built by experts in language and AI, Trinka goes beyond basic grammar checks to improve clarity, tone, and word choice. It corrects complex errors, follows academic style guides, and provides intelligent writing suggestions in real time. Trinka offers tools such as AI-assisted writing, a Paraphraser, AI Studio, Consistency Checks, Technical Checks, Language Quality Checks, Citation Checks, and a Proofread File feature to help users create polished, publication-ready documents. Available as a web app, browser plugin, Word add-in, and desktop app, Trinka also provides enterprise solutions with customization, privacy, and integration options.

About the team

We are a bunch of passionate researchers, engineers, product managers, data scientists, and designers who came together to build a product that can revolutionize the way any research-intensive projects are done. Reducing cognitive load and helping people to convert information into knowledge, is at the core of our mission. Our engineering team is building a scalable platform that deals with tons of data, AI processing over the data, and interactions of users from across the globe. We believe research plays a key role in making the world a better place, and we want to make it easy to approach and fun to do!

We are looking for a highly motivated AI / Data Science Engineer with 2–5 years of hands-on experience in building and deploying intelligent systems. The ideal candidate should have strong expertise in Large Language Models (LLMs), model serving (including vLLM), and agent-based architectures. This role involves designing scalable AI solutions, optimizing inference pipelines, and building production-grade AI systems.

Key Responsibilities:

• Design, develop, and deploy AI/ML models with a focus on Large Language Models (LLMs)
• Implement and optimize high-performance model serving solutions (e.g., vLLM, FastAPI, Triton)
• Build and maintain agent-based systems (Agentic AI, RAG pipelines, multi-agent workflows)
• Develop scalable data pipelines for preprocessing, embedding generation, and retrieval
• Work on vector databases (FAISS, ChromaDB, etc.) for similarity search and RAG use cases
• Optimize inference latency, throughput, and cost for production deployments
• Collaborate with cross-functional teams to integrate AI solutions into products
• Monitor, debug, and improve deployed AI systems for reliability and performance
• Stay updated with the latest advancements in LLMs, multimodal AI, and agent frameworks

Required Skills & Qualifications

• Minimum 1 year of hands-on experience working with Agentic AI systems (multi-agent workflows, autonomous agents, or orchestration frameworks)
• 2–5 years of experience in AI / Data Science / Machine Learning
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face)
• Hands-on experience with Large Language Models (LLMs) and prompt engineering
• Experience with vLLM or similar high-performance inference engines
• Practical knowledge of building agent-based systems (LangChain, CrewAI, custom agents, etc.)
• Experience with Retrieval-Augmented Generation (RAG) architectures
• Familiarity with vector databases (FAISS, ChromaDB, Pinecone, etc.)
• Strong understanding of REST APIs and backend frameworks (FastAPI preferred)
• Experience with Docker, Kubernetes, or cloud deployments (AWS/GCP/Azure)
• Solid understanding of data structures, algorithms, and system design

Preferred / Good-to-Have Skills

• Experience with multimodal models (LLaVA, image-to-text, OCR pipelines, etc.)
• Knowledge of distributed systems and GPU optimization techniques
• Experience with streaming inference and real-time AI systems
• Familiarity with model quantization, batching, and memory optimization
• Exposure to open-source LLM ecosystems (OpenRouter, vLLM, TGI, etc.)
• Experience with logging, monitoring, and observability tools
• Knowledge of CI/CD pipelines for ML systems (MLOps)

Education

Minimum qualification: B.Tech / B.E. in Computer Science, Electrical Engineering, or a related field