Machine Learning Engineer

MaxHome.AI; Bengaluru; On-site; INR 1,500,000 - 2,700,000; Full time

Job summary

MaxHome.AI in Bengaluru, India is seeking an experienced Machine Learning Engineer to build production-grade AI solutions. You will design scalable Python services, integrate LLMs, and develop Voice AI and document intelligence workflows for real-estate applications.

The role requires hands-on experience with OCR, RAG systems, and cloud-native ML infra. Collaboration with product, engineering, and operations teams is essential for reliable, cost-efficient deployments.

Qualifications

• 3-6 years hands-on ML engineer experience.
• Expert Python programming and clean coding practices.
• Experience designing and integrating production APIs.
• Experience integrating LLM models and prompt engineering.
• Knowledge of model fine-tuning and inference optimization.
• Hands-on expertise in large-scale document processing.
• Healthcare document workflows experience is a plus.
• Experience with Docker, containers, and Kubernetes.
• Understanding of microservices and cloud infrastructure.
• Strong debugging and problem-solving across the ML lifecycle.

Responsibilities

• Design and implement production-grade API services for AI workloads.
• Integrate LLMs from providers such as OpenAI and Anthropic.
• Develop scalable Voice AI workflows and RAG systems.
• Optimize inference latency, memory usage, and costs.
• Build OCR and document understanding pipelines.
• Collaborate with Product and Operations to translate requirements into ML solutions.
• Contribute to CI/CD pipelines and testing for reliability.
• Mentor junior engineers and own end-to-end projects.

Skills

Python

Generative AI

Voice AI

LLM systems

Document processing

Backend APIs

ML Infra

Docker

Kubernetes

REST APIs

FastAPI

Flask

Tools

FastAPI

Flask

Docker

Kubernetes

REST APIs

Job description

We are looking for an experienced Machine Learning Engineer with strong expertise in Python, Generative AI, Voice AI, LLM systems, document intelligence, and backend engineering. The ideal candidate will have hands‑on experience building and deploying production‑grade AI solutions, integrating LLMs, developing scalable APIs, and managing ML infrastructure. You will work closely with Product, Engineering, Design, and Operations teams to build reliable, scalable, and cost‑efficient AI applications.

The candidate will have responsibilities across the following functions:

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 Retrieval‑Augmented Generation (RAG) and knowledge‑grounded systems where required.
• Define and improve evaluation frameworks for response quality, latency, accuracy, safety, and cost.
• Profile and optimise inference performance, including latency, throughput, memory usage, and model cost.
• Fine‑tune or adapt models when appropriate using techniques such as supervised fine‑tuning, quantisation, distillation, and other inference‑optimisation 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 optimise API performance in production.
• Write clean, maintainable, well‑tested code and contribute to engineering standards.

Infrastructure and MLOps:

• Containerise 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:

• 3-6 years of hands‑on experience as an ML Engineer or in a similar role.
• Expert‑level Python programming skills and clean coding practices.
• Strong experience designing and integrating production APIs.
• Practical experience integrating LLM models and developing optimised prompts.
• Strong understanding of model fine‑tuning, hyperparameter tuning, and inference optimisation.
• Hands‑on expertise in large‑scale document processing and classification.
• Healthcare document workflow experience will be a strong advantage.
• Experience with Docker, containerised deployments, and Kubernetes orchestration.
• Good understanding of microservices architecture, distributed systems, and cloud infrastructure.
• Strong problem‑solving and debugging skills across the ML lifecycle.

Nice‑to‑Have Skills:

• Experience with vector databases such as Pinecone, Weaviate, or FAISS.
• Experience with event‑driven architecture using Kafka, Pub/Sub, SQS, or SNS.
• Exposure to data pipeline orchestration tools such as Airflow, Prefect, or Dagster.