Role Overview
The Senior AI / ML Engineer will serve as the principal technical builder within Roswalt's AI Cell. The ideal candidate has deep hands-on experience developing and deploying machine learning systems and LLM-powered applications into production environments with real users.
You will be responsible for building scalable AI products, implementing document intelligence pipelines, developing internal RAG and agentic systems, and ensuring production-grade reliability across deployed models and applications.
This role requires someone who combines strong engineering fundamentals with practical AI deployment experience and the ability to deeply understand business workflows before building solutions.
Key Responsibilities
AI Model & Application Development
- Design, develop, train, fine-tune, and deploy AI/ML models and LLM-powered applications for production use.
- Build scalable AI systems with measurable performance, latency, reliability, and uptime standards.
- Deliver production-ready AI applications integrated into business operations.
Document AI & Intelligent Automation
- Develop Document AI pipelines for legal and operational documents including:
- RERA Filings
- PAAA
- Allotment Letters
- Consent Letters
- Rental Agreements
- Vendor Contracts
- Implement OCR, classification, extraction, summarization, and validation workflows.
Lead Scoring & Predictive Systems
- Build and deploy Roswalt's internal Lead Scoring Engine integrated with PMS and sales workflows.
- Work closely with Sales and Business Development teams to improve lead qualification and conversion intelligence.
LLM, RAG & Agentic Systems
- Build and maintain internal Retrieval-Augmented Generation (RAG) systems and AI agents.
- Implement prompt engineering, evaluation frameworks, regression testing, guardrails, and model-update protocols.
- Evaluate and optimize open-source and proprietary LLMs for enterprise use cases.
Production Engineering & Reliability
- Ensure all AI systems meet agreed SLAs for performance, uptime, and reliability.
- Maintain clean, scalable, reviewed, and version-controlled codebases.
- Conduct root-cause analysis and continuously improve production stability.
Cross-Functional Collaboration
- Work closely with departments including Sales, Legal, Construction, CRM, and Operations to understand workflows before building solutions.
- Translate business problems into scalable AI-driven systems.
Knowledge Sharing & Engineering Culture
- Document architectures, deployment pipelines, and shipped systems thoroughly.
- Conduct regular technical knowledge-sharing sessions with internal teams.
Key Result Areas (KRAs)
- Ship at least 3 production-grade AI/ML models or LLM applications within Year 1.
- Deliver a fully operational Document AI pipeline within 120 days.
- Build and deploy Roswalt's Lead Scoring Engine within 180 days.
- Establish and maintain internal RAG and agentic AI infrastructure.
- Maintain production reliability with strong SLA adherence and incident accountability.
- Contribute to a scalable and maintainable AI engineering culture.
Required Qualifications
- 5–8 years of hands-on experience building and deploying Machine Learning systems.
- Minimum 2 years of experience working with:
- LLMs
- Transformers
- Generative AI systems
- Bachelor's or Master's degree in:
- Computer Science
- Engineering
- Mathematics
- Statistics
- or related technical disciplines
- Strong preference for candidates from top engineering institutes including IITs, IIITs, BITS, NITs, IISc, ISI, or equivalent institutions.
Technical RequirementsStrong expertise in:
- Python
- Machine Learning system design
- Model deployment and optimization
- API development and integrations
Production experience with at least two of:
- PyTorch
- TensorFlow
- Hugging Face Transformers
- LangChain
- LlamaIndex
- Vector Databases (Pinecone, Weaviate, pgvector)
- OpenAI APIs
- Anthropic APIs
- Open-source LLMs
Infrastructure & Deployment
- AWS, GCP, or Azure
- Docker
- Basic Kubernetes
- CI/CD pipelines for ML systems
- Model monitoring and observability
Mandatory Expectation
Candidates must have personally taken AI/ML systems from experimentation to production deployment and should be able to explain architecture, deployment, monitoring, and business impact in detail.
Preferred Qualifications
- Experience with Computer Vision applications such as:
- CCTV analytics
- Drone-based construction monitoring
- Experience with:
- Conversational AI
- Voice AI
- Multilingual NLP
- Indian language understanding capabilities (Hindi, Marathi, Gujarati) will be a strong advantage.
- Open-source contributions, Kaggle achievements, published research, or real-world AI case studies are highly valued.
Disqualifiers
Candidates may not be considered if:
- Their experience is limited to using pre-built AI APIs without understanding underlying systems.
- Their background is primarily BI, MIS, reporting, or analytics work re-labelled as AI.
- They are unable to demonstrate production-level AI work, architecture understanding, or deployment ownership.
What We're Looking For
We are looking for someone who:
- Thinks like an engineer and acts like an owner.
- Can independently build and ship production-grade AI systems.
- Understands both technical depth and business impact.
- Is highly execution-focused, practical, and accountable.
- Wants to build meaningful AI products from the ground up.
If you are passionate about solving real-world business problems using AI and want to help shape the future of AI adoption within a fast-growing real estate organization, we would love to hear from you.
