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.

- Build AI systems for OCR, document understanding, information extraction, and classification.

- 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)