At a Glance
- Tasks: Join as an AI/ML Engineer to build and optimise cutting-edge voice AI technology.
- Company: Be part of a pioneering telecom venture backed by global industry leaders.
- Benefits: Enjoy flexible salaries, exceptional packages, and a collaborative work environment.
- Why this job: Shape the future of voice AI while working with top-tier models and experts.
- Qualifications: Strong Python skills and experience with AI/ML APIs are essential; a Master's in ML or AI is preferred.
- Other info: Based in London, with a hybrid work model of 3 days on-site.
The predicted salary is between 80000 - 120000 £ per year.
Job Description
AI/ML Engineer – Voice AI/LLMs, Speech-to-Text, TTS
My client is building the future of voice AI in telecoms and are looking for an exceptional AI/ML Engineer to join them as part of the founding team.
The company is backed by a number of industry leading Global enterprises who have joined forces to build cutting edge technology together.
This is your chance to shape and help scale a brand-new venture at the cutting edge of telecommunications and artificial intelligence. You'll be the technical owner of their AI stack, integrating and optimizing world-class models from providers like OpenAI, Google, Anthropic, Deepgram, ElevenLabs, and more.
- Own the AI Pipeline: From STT LLM TTS, you'll build, test, and optimize the voice AI stack.
- Integrate Leading Models: Leverage APIs from top providers across Speech-to-Text, Text-to-Speech, Voice Cloning, and Large Language Models.
- Benchmark + Optimize: Curate golden datasets to evaluate model accuracy, latency, and cost for real-world telecom use cases.
- Enhance Personalization: Develop AI features for emotional intelligence, cultural nuances, and regional accents.
- Collaborate Cross-Functionally: Work side-by-side with architects, Back End engineers, telco experts, and product managers.
Candidates should have experience in some of the following areas:
- Strong hands-on experience integrating AI/ML APIs into production systems
- Strong Python skills + comfort with data science libraries (Pandas, NumPy, etc.)
- Experience with voice-related AI (STT, TTS, or Voice AI)
- Deep understanding of latency vs accuracy vs cost trade-offs
- Familiar with prompt engineering and RAG techniques
- Background in microservices, APIs, and cloud-based deployments
- Experience with MLOps tools and monitoring platforms (eg, DataDog, Arize)
Any experience in the following areas is a nice to have:
- Experience with Voice LMMs (like GPT-4o, Nova Sonic)
- Previous work in telecommunications or Real Time speech systems
- Familiarity with voice cloning, multilingual models, or PEFT techniques
- Knowledge of privacy and security protocols for voice data
- Master's degree in ML, AI, or a related field
This is a career defining role and an opportunity to create something truly special. It is based in London with offices near Barbican and 3 days a week on-site on average. Salaries are flexible to attract the best talent Globally with a guide of between £100-130K basic + exceptional package & bonus
AI/ML Engineer - Voice AI/LLMs, Speech-to-Text, TTS employer: Ventula Consulting
Contact Detail:
Ventula Consulting Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI/ML Engineer - Voice AI/LLMs, Speech-to-Text, TTS
✨Tip Number 1
Familiarise yourself with the latest advancements in voice AI technologies, especially those from providers like OpenAI and Google. This knowledge will not only help you during interviews but also demonstrate your genuine interest in the field.
✨Tip Number 2
Engage with online communities and forums related to AI/ML, particularly those focused on voice technologies. Networking with professionals in these spaces can lead to valuable insights and potential referrals.
✨Tip Number 3
Prepare to discuss specific projects where you've integrated AI/ML APIs into production systems. Be ready to explain your approach to optimising models for latency, accuracy, and cost, as this is crucial for the role.
✨Tip Number 4
Showcase your understanding of MLOps tools and monitoring platforms by discussing any relevant experience you have. Highlighting your familiarity with these tools can set you apart from other candidates.
We think you need these skills to ace AI/ML Engineer - Voice AI/LLMs, Speech-to-Text, TTS
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI/ML, particularly with voice-related technologies like Speech-to-Text and Text-to-Speech. Use specific examples of projects where you've integrated AI/ML APIs or worked with data science libraries.
Craft a Compelling Cover Letter: In your cover letter, express your passion for voice AI and telecommunications. Mention how your skills align with the company's goals and how you can contribute to building their AI stack. Be sure to include any experience with latency vs accuracy trade-offs and MLOps tools.
Showcase Relevant Projects: If you have worked on projects involving voice AI, LLMs, or related fields, summarise these in your application. Highlight your role, the technologies used, and the outcomes achieved. This will demonstrate your hands-on experience and technical ownership.
Highlight Collaboration Skills: Since the role involves cross-functional collaboration, mention any experience working with diverse teams, such as architects, engineers, and product managers. Provide examples of how you effectively communicated and contributed to team success in previous roles.
How to prepare for a job interview at Ventula Consulting
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with AI/ML APIs and Python. Highlight specific projects where you've integrated these technologies, especially in voice-related AI like STT or TTS.
✨Understand the Trade-offs
Familiarise yourself with the latency vs accuracy vs cost trade-offs in AI models. Be ready to explain how you would approach these challenges in real-world telecom scenarios.
✨Demonstrate Collaboration Skills
Since the role involves working cross-functionally, think of examples where you've successfully collaborated with engineers, product managers, or other stakeholders. Emphasise your ability to communicate technical concepts clearly.
✨Prepare for Problem-Solving Questions
Expect questions that assess your problem-solving skills, particularly in optimising AI pipelines. Practice articulating your thought process when faced with challenges related to model integration or performance benchmarking.