{"id":3675389,"url":"https://alion.io/job/maxhome-ai-machine-learning-engineer-voice-document","title":"Machine Learning Engineer - voice / Document","company":{"id":6725,"name":"MaxHome.AI","domain":"maxhome.ai","url":"https://alion.io/company/maxhome-ai","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"OCR","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"SFT","optional":false},{"name":"Speech Recognition","optional":false},{"name":"Text-to-Speech","optional":false}],"status":"live","first_seen_at":"2026-11-03T10:35:20Z","employer_posted_date":null,"last_verified_at":"2026-11-03T10:35:20Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"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.\n\nThe core responsibilities for the job include the following:\n\nVoice AI and LLM systems: \nDesign and develop conversational Voice AI systems using ASR, TTS, LLMs, and workflow orchestration.\nIntegrate models from providers such as OpenAI, Anthropic, and other speech and AI platforms.\nBuild reliable voice workflows for use cases such as lead qualification, follow-ups, scheduling, and transaction support.\nDesign prompt strategies, tool-calling flows, context management, and reusable prompt libraries.\nBuild RAG and knowledge-grounded systems where required.\nDefine and improve evaluation frameworks for response quality, latency, accuracy, safety, and cost.\nProfile and optimize inference performance, including latency, throughput, memory usage, and model cost.\nFine-tune or adapt models when appropriate using techniques such as supervised fine-tuning, quantization, distillation, or other inference-optimization methods.\n\nDocument Intelligence: \nBuild AI systems for OCR, document classification, information extraction, and document understanding.\nDevelop workflows that convert unstructured real-estate documents into structured, actionable data.\nImprove extraction accuracy through better data preparation, prompting, model selection, validation, and evaluation.\nCollaborate with product and operations teams to translate business and compliance requirements into ML solutions.\n\nBackend engineering: \nBuild scalable backend services and APIs using Python and frameworks such as FastAPI or Flask.\nDesign REST APIs and integrations for AI, voice, and document-processing workflows.\nImplement authentication, rate limiting, retries, queuing, and failure handling.\nDebug, profile, and optimize API performance in production.\nWrite clean, maintainable, well-tested code and contribute to engineering standards.\n\nInfrastructure and MLOps: \nContainerize applications and services using Docker.\nDeploy and operate ML and backend services on Kubernetes or managed cloud platforms.\nContribute to CI/CD pipelines, automated testing, and safe release processes.\nImplement logging, monitoring, alerting, and service-level metrics for AI systems.\nHelp manage model and service deployments across development, staging, and production environments.\nContribute to scalable, reliable, and cost-efficient cloud infrastructure.\n\nCollaboration and ownership: \nWork closely with product, engineering, design, and operations teams to solve real business problems.\nConvert ambiguous requirements into practical technical solutions.\nParticipate in architecture discussions and make thoughtful engineering trade-offs.\nProvide technical guidance and mentorship to junior engineers.\nTake ownership of projects from design and experimentation through deployment and ongoing improvement.\n\nRequirements: \n4.5 to 7 years of hands-on experience as an ML engineer or in a similar role.\nExpert-level Python programming and clean code practices.\nStrong experience designing and integrating production APIs.\nPractical experience integrating LLM models and writing optimized prompts.\nStrong understanding of model fine-tuning, hyperparameter tuning, and inference optimization.\nHands-on expertise in large-scale document processing and classification, with healthcare document workflow experience being a strong plus.\nExperience with Docker, containerised deployments, and Kubernetes orchestration.\nGood understanding of microservices architecture, distributed systems, and cloud infrastructure.\nSolid problem-solving and debugging skills across the ML lifecycle.\nSkills\nai, anthropic, APIs, Architecture, ASR, cicd, cloud, compliance, Deployment, Distillation, Distributed Systems, Docker, Document Parsing, FastAPI, Flask, FORAN, Healthcare, ISA, Kubernetes, Lead Qualification, lifecycle, llm, LLMs, Logging, Machine Learning, Microservices, ml, MLOps, Monitoring, OCR, OpenAI, operations, Orchestration, production, Python, pythonprogramming, quality, SAFe, Safety, shipping, Speech Recognition, STT, testing, TTS, voice, Voice AI","description_format":"text","description_chars":4829,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["PropTech","Real Estate AI"],"lifecycle":[{"event":"open","at":"2026-11-03T10:35:20Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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