Machine Learning Engineer - Voice AI

MaxHome.AI; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

MaxHome.AI is seeking a Senior Machine Learning Engineer (3 to 6 years) to lead the design, development, and deployment of AI-powered systems, blending hands-on ML engineering with backend development and production-grade infrastructure.

You will integrate LLMs, build Voice AI pipelines, design robust prompts, and push models into production while collaborating with cross-functional teams and mentoring junior engineers.

Qualifications

• 3–6 years of hands-on ML engineer experience.
• Expert Python programming and clean-code practices.
• Production API design and integration experience.
• Experience integrating LLMs and writing prompts.
• Knowledge of fine-tuning, hyperparameters, and inference optimization.
• Docker, containers, and Kubernetes orchestration experience.
• Microservices, distributed systems, and cloud infrastructure understanding.
• Strong problem-solving and debugging across the ML lifecycle.

Responsibilities

• Design, develop, and deploy AI-powered systems.
• Lead ML and LLM pipelines, prompts, and RAG workflows.
• Build scalable backend services in Python (FastAPI/Flask).
• Containerize with Docker and deploy on Kubernetes.
• Collaborate with cross-functional teams and mentor juniors.
• Ensure production readiness and high engineering standards.

Skills

Python programming

API design

ML engineering

Problem solving

Tools

Docker

Kubernetes (EKS/GKE/AKS)

FastAPI/Flask

LLM integration

Job description

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).