AI Engineer

LLM Applications & AI Agent Development

• Build and deploy LLM-powered bots, agents, and NLP query interfaces for business use cases
• Integrate Claude, OpenAI, and other LLM APIs into production workflows
• Design agentic workflows with tool-use, retrieval-augmented generation (RAG), and multi-step reasoning
• Develop and maintain AI integrations with Microsoft Teams, CRM, ERP, and internal platforms

Data Engineering & Analytics

• Build and maintain data pipelines connecting ERP, CRM, Slate, and the Qube Data Lake
• Write complex SQL queries, design data models, and optimise analytical workloads
• Use Python (pandas, dbt, SQLAlchemy) and R for data transformation, modelling, and visualisation
• Support BI dashboard development with clean, well-structured data layers

AI & ML Model Development

• Develop, train, and evaluate machine learning and deep learning models
• Work on applied computer vision tasks including super-resolution (2K to 4K upscaling)
• Set up GPU training pipelines, manage datasets, and track experiments (MLflow or equivalent)
• Collaborate with research partners (e.g., IIT Madras) to operationalise research outputs

Automation & Integration Engineering

• Build and maintain workflow automation pipelines for business process automation
• Develop REST API integrations, webhooks, and event-driven pipelines between systems
• Implement and manage low-code automation tools as part of broader engineering workflows
• Create reusable automation scripts and CI-friendly deployment patterns

Collaboration & Documentation

• Work closely with the Lead — Digital Transformation to translate business requirements into technical solutions
• Document model performance, data schemas, API contracts, and deployment guides
• Participate in architecture reviews and technical decision-making

Technical Skills

Core (Required)

• Python — pandas, NumPy, scikit-learn, FastAPI, SQLAlchemy, Jupyter
• R — data wrangling, statistical modelling, ggplot2, Shiny (for dashboards)
• SQL — complex queries, window functions, CTEs; experience with data warehouses (Snowflake, BigQuery, PostgreSQL)
• LLM APIs — OpenAI, Anthropic Claude API; prompt engineering, function/tool calling, RAG pipelines
• REST API development and consumption; JSON, OAuth, webhook patterns
• Git, GitHub/GitLab — version control, code review, CI pipelines
• Linux/Bash — scripting, cron, server-side automation
• Deep learning frameworks — PyTorch or TensorFlow for model training

Added Advantage

• Claude Code (Anthropic) — agentic coding, custom agent development, MCP integrations
• Emergent AI — platform familiarity and agent deployment experience
• n8n — workflow automation orchestration, custom nodes, API chaining
• Computer vision — image super-resolution, ESRGAN, Real-ESRGAN, or similar architectures
• MLflow, DVC, or similar — experiment tracking, model registry, reproducibility
• Docker, containerisation — packaging models and services for deployment
• Blockchain / Web3 basics — familiarity with Hedera or similar for IP/rights use cases
• Vector databases — Pinecone, Weaviate, pgvector for embedding-based retrieval

What You Will Work On
Active projects include: an NLP sales query bot integrated with Microsoft Teams; a 2K-to-4K AI super-resolution model in collaboration with IIT Madras; P&L and revenue analytics dashboards on ERP and Data Lake; CRM data pipeline engineering across ERP, Qube Central, and Slate; and IP rights management using blockchain architecture. You will ship across all of these.Qualifications

• B.Tech / M.Tech / M.Sc. in Computer Science, Data Science, Electrical Engineering, or related field
• 2–5 years of hands-on experience in AI engineering, data engineering, or applied ML roles
• Strong portfolio or demonstrated experience building and shipping AI-powered products
• Comfort working in a fast-paced, cross-functional team with shifting priorities

What We Offer

• Hands-on work with frontier AI tooling — LLMs, agents, computer vision, and more
• Collaboration with IIT Madras on applied research projects
• Direct ownership of AI products used across thousands of cinema screens
  • Competitive compensation, flexible working,