Machine Learning Engineer - voice / Document

We are looking for a Machine Learning Engineer, Voice AI/document intelligence platform to design, build, and deploy production-grade AI systems for real estate workflows. You will work across speech recognition, text-to-speech, LLM orchestration, conversational AI, evaluation, and backend services. You will take AI systems from prototype to production by building reliable APIs, improving latency and quality, and operating services at scale. Although Voice AI is the primary focus of this role, you will also contribute to MaxHome. AI's broader Document Intelligence platform, which supports OCR, document understanding, information extraction, compliance checks, and transaction automation. This is a hands-on role for an engineer who enjoys solving ambiguous problems, shipping quickly, and owning systems end to end.

The core responsibilities for the job include the following:

Voice AI and LLM systems:

• Design and develop conversational Voice AI systems using ASR, TTS, LLMs, and workflow orchestration.
• Integrate models from providers such as OpenAI, Anthropic, and other speech and AI platforms.
• Build reliable voice workflows for use cases such as lead qualification, follow-ups, scheduling, and transaction support.
• Design prompt strategies, tool-calling flows, context management, and reusable prompt libraries.
• Build RAG and knowledge-grounded systems where required.
• Define and improve evaluation frameworks for response quality, latency, accuracy, safety, and cost.
• Profile and optimize inference performance, including latency, throughput, memory usage, and model cost.
• Fine-tune or adapt models when appropriate using techniques such as supervised fine-tuning, quantization, distillation, or other inference-optimization methods.

Document Intelligence:

• Build AI systems for OCR, document classification, information extraction, and document understanding.
• Develop workflows that convert unstructured real-estate documents into structured, actionable data.
• Improve extraction accuracy through better data preparation, prompting, model selection, validation, and evaluation.
• Collaborate with product and operations teams to translate business and compliance requirements into ML solutions.

Backend engineering:

• Build scalable backend services and APIs using Python and frameworks such as FastAPI or Flask.
• Design REST APIs and integrations for AI, voice, and document-processing workflows.
• Implement authentication, rate limiting, retries, queuing, and failure handling.
• Debug, profile, and optimize API performance in production.
• Write clean, maintainable, well-tested code and contribute to engineering standards.

Infrastructure and MLOps:

• Containerize applications and services using Docker.
• Deploy and operate ML and backend services on Kubernetes or managed cloud platforms.
• Contribute to CI/CD pipelines, automated testing, and safe release processes.
• Implement logging, monitoring, alerting, and service-level metrics for AI systems.
• Help manage model and service deployments across development, staging, and production environments.
• Contribute to scalable, reliable, and cost-efficient cloud infrastructure.

Collaboration and ownership:

• Work closely with product, engineering, design, and operations teams to solve real business problems.
• Convert ambiguous requirements into practical technical solutions.
• Participate in architecture discussions and make thoughtful engineering trade-offs.
• Provide technical guidance and mentorship to junior engineers.
• Take ownership of projects from design and experimentation through deployment and ongoing improvement.

Requirements:

• 4.5 to 7 years of hands-on experience as an ML engineer or in a similar role.
• Expert-level Python programming and clean code practices.
• Strong experience designing and integrating production APIs.
• Practical experience integrating LLM models and writing optimized prompts.
• Strong understanding of model fine-tuning, hyperparameter tuning, and inference optimization.
• Hands-on expertise in large-scale document processing and classification, with healthcare document workflow experience being a strong plus.
• 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.