Our Clients looking to bring on a Senior Machine Learning Engineer to help build and scale a next‑generation voice-centric AI platform used by millions. In this role, you’ll own the full ML lifecycle-from research and data pipelines to training, deployment, optimisation, and evaluation-while pushing the boundaries of real‑time conversational AI.

You’ll focus on creating highly optimised, domain-specific conversational models, fine‑tuning and compressing existing LLMs and speech models to deliver sub‑second, natural interactions at scale. This is an opportunity for someone who has taken STT/TTS/LLM dialogue systems from prototype to production and thrives in fast iteration, deep optimisation, and real-world impact.

What You’ll Do

• Build and optimise STT, TTS, and LLM pipelines that sustain <800 ms latency across thousands of concurrent voice sessions.
• Apply LoRA, QLoRA, DPO, RLHF, quantisation, pruning, and distillation to create smaller, faster, cheaper conversational models.
• Fine‑tune and evaluate lightweight architectures (e.g., DeBERTa, SetFit, sentence transformers) for intent classification and dialogue management.
• Optimise inference on GPUs/CPUs using dynamic batching, speculative decoding, and streaming gRPC/WebSockets.
• Develop data pipelines (Kafka → Snowflake/S3) and build evaluation frameworks for ASR/WER, TTS MOS, and task‑completion metrics.
• Implement and extend dialogue flows using LangGraph/LangChain and Model Context Protocol (MCP).
• Establish ML best practices: versioning, monitoring, CI/CD for models, safety guardrails, and production reliability.
• Work closely with product, infra, compliance, and customer teams to deliver safe, scalable conversational experiences.
• Stay on the cutting edge of speech + LLM research and rapidly prototype new methods (e.g., Whisper-v3, Bark, efficient fine‑tuning techniques).
• Ensure HIPAA‑grade security, PHI-safe handling, and always-on reliability.

• Are you interested in this position?

   

  Apply by clicking on the “Apply Now” button below!

   

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