Machine Learning Engineer - Voice AI

We're looking for a Senior Machine Learning Engineer (3 to 6 years) to lead the design, development, and deployment of AI-powered systems. This role combines hands-on ML engineering, backend development, LLM integration, and production-grade infrastructure.

The candidate will have responsibilities across the following functions:

Machine Learning and LLMs:

• Integrate LLMs (OpenAI, Anthropic, etc. ) into pipelines; prompting, workflows, RAG, evaluation, and iteration.
• Architect and develop Voice AI systems using technologies such as ASR, TTS, LLMs, and conversational AI.
• Design robust prompt engineering strategies and maintain prompt libraries across environments.
• Improve model performance via fine-tuning, quantisation, pruning, or distillation when needed.
• Build, train, fine-tune, and optimise ML and LLM-based models for production use cases.

Backend Engineering:

• Develop scalable backend systems using Python (FastAPI/Flask preferred).
• Architect and integrate REST APIs, rate limiting, and monitoring.
• Debug, profile, and optimise API performance in production.

Infrastructure and DevOps:

• Build and deploy containerised applications using Docker.
• Manage model and service deployments on Kubernetes (EKS, GKE, AKS or self-managed clusters).
• Work with CI/CD pipelines to ensure smooth releases and automated testing.
• Implement logging, monitoring, and alerting for ML and backend services.

Collaboration and Leadership:

• Work closely with cross-functional teams to convert business problems into ML solutions.
• Provide technical guidance to junior engineers and contribute to architectural decisions.
• Bring a strong bias for shipping, iteration, and maintaining high engineering standards.

Requirements:

• 3 to 6 years of hands-on experience as an ML Engineer or similar role.
• Expert-level Python programming and clean code practices.
• Strong experience designing and integrating production APIs.
• Practical experience integrating LLM models and writing optimised prompts.
• Strong understanding of model fine-tuning, hyperparameter tuning, and inference optimisation.
• Experience with Docker, containerised deployments, and Kubernetes orchestration.
• Good understanding of microservices architecture, distributed systems, and cloud infrastructure.
• Solid problem-solving and debugging skills across the ML lifecycle.

Nice-to-Have:

• Experience with vector databases (Pinecone, Weaviate, FAISS).
• Experience with event-driven architecture (Kafka, Pub/Sub, SQS/SNS).
• Exposure to data pipelines (Airflow, Prefect, Dagster).