Abrar Admin

ConnexAI

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 Build Low Latency Conversational AI Systems
 
 
 
 We are building real-time conversational AI systems built on top of large language models, speech AI, and agentic workflows. Our platform combines ASR, LLMs, and TTS into production-grade AI systems used globally across enterprise environments where latency, reliability, and scalability matter.
 
 
 
 
 
 We are hiring a Machine Learning Engineer to build low-latency production systems for our LLM team. This role is centred around writing scalable code that enables real-time conversational AI to perform reliably under heavy production workloads.
 
 
 
 
 
 You&rsquo;ll work closely with our LLM and speech teams to solve challenges around inference speed, concurrency, request handling, GPU performance, distributed systems, and real-time response streaming.
 
 
 
 
 
 
 What you&rsquo;ll do
 
 
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 Build and optimise low-latency LLM systems for real-time conversational AI
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 Write production-grade Python code focused on performance, scalability, and reliability
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 Design systems capable of handling large volumes of concurrent real-time requests
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 Solve engineering challenges around batching, request scheduling, queue management, streaming responses, and distributed workloads
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 Improve inference speed, GPU memory usage, and overall system responsiveness
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 Deploy and optimise open-source LLMs using tooling such as vLLM, TensorRT-LLM, Triton, SGLang, CUDA, or similar technologies
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 Build scalable orchestration layers and ML pipelines around LLM systems, including RAG and agentic workflows
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 Develop backend inference services and APIs for production AI systems
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 Productionise new model capabilities and features for real-world customer use cases
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 What we&rsquo;re looking for
 
 
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 Strong experience writing production-grade software for machine learning systems
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 Strong Python engineering skills
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 Experience building low-latency or highly concurrent systems
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 Strong problem-solving ability and enjoyment of building systems from the ground up
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 Experience with distributed systems, parallel workloads, and performance optimisation
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 Experience working with inference tooling such as vLLM, TensorRT, Triton, CUDA, ONNX, or similar technologies
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 Experience building scalable backend services or ML systems used in production
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 Understanding of real-time systems and performance-focused engineering
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 Strong communication skills and ability to work closely with engineers and researchers
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 Why this role?
 
 
 
 You&rsquo;ll work on designing and building low-latency conversational AI systems capable of serving large volumes of concurrent real-time requests. The role focuses on solving difficult engineering challenges around inference speed, reliability, concurrency, GPU performance, and scalable production AI systems.
 
 
 
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Machine Learning Engineer

<div>
 
 
 AI Engineer - Feature Prototyping
 
 
 
 
 
 
 We are building Conversational AI products powered by large language models, and we are always striving to provide the best features for our clients. This means there&rsquo;s always a new problem to solve, a new solution to find, and a new way to translate LLM capabilities into real product features.
 
 
 
 
 
 We are hiring an AI Engineer to help bring these new products to life.
 
 
 
 
 
 You will sit on our LLM AI team, bridging the gap to our software engineering teams, turning model capabilities into working prototypes and clear reference implementations that can later be built into production systems.
 
 
 
 
 
 Your work will define how LLMs are actually used across our products. You will take ideas and turn them into real, testable systems that engineers can rely on.
 
 
 
 
 
 
 What you will do
 
 
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 Improve the quality of conversational AI features by making them more accurate, consistent, and responsive through hands-on prototyping
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 Work closely with the LLM team to understand how the models behave, where they perform well, and where they break, and to experiment with new features and methods
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 Build working prototypes that show how LLM-powered features should function in real product flows
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 Design and test prompt structures, input and output formats, and interaction patterns such as retrieval-based systems and agent-style workflows
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 Create reference implementations, such as APIs or small services, that give engineering teams something concrete to build from
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 Turn high-level product ideas into clear and testable systems
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 Identify edge cases, failure points, and user experience issues early through hands-on prototyping
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 Work closely with frontend and backend engineers to ensure that what is designed can be built and scaled properly
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 What we are looking for
 
 
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 Someone who enjoys turning ideas into working systems
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 Someone passionate about Conversational AI
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 Comfortable writing code and building prototypes quickly, most commonly in Python
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 Experience working with large language models in real applications
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 Understanding of how these models behave in practice, including limitations such as inconsistency, latency, and unexpected outputs
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 Ability to design systems that work around these limitations
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 Product-minded, with the ability to think about how something works for a user, not just in isolation
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 Backgrounds that tend to fit well
 
 
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 Data scientists who have moved into LLM or applied AI work
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 Physics, maths, or other quantitative science backgrounds with strong coding skills (PhD or industry)
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 Machine learning engineers with product exposure
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 Applied AI or LLM engineers
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 Backend engineers who have worked closely with LLM-based features
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AI Engineer

Join ConnexAI and explore 2   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

ConnexAI

About Company

Machine Learning Engineer

AI Engineer