GenAI Developer

Job Description

About Company : Our Client is a leading Indian multinational IT services and consulting firm. It provides digital transformation, cloud computing, data analytics, enterprise application integration, infrastructure management, and application development services. The company caters to over 700 clients across industries such as banking and financial services, manufacturing, technology, media, retail, and travel & hospitality. Its industry-specific solutions are designed to address complex business challenges by combining domain expertise with deep technical capabilities. With a global workforce of over 80,000 professionals and a presence in more than 50 countries. Job Title: GenAI Developer Locations: PAN INDIA Experience: 4-8 Years Employment Type: Contract to Hire Work Mode : Work From Office Notice Period : Immediate to 15 Days Job Description: Role Summary: We?re seeking a forward-thinking developer with 4-8 years of experience to design and deploy GenAI-driven geospatial solutions. This role emphasizes computer-vision, cloud-native ML workflows, multi-agent systems and generative AI integration for geospatial intelligence. Core Responsibilities: Develop GenAI-first applications for geospatial analytics, leveraging LLMs and multi-agent architectures. Build intelligent computer vision pipelines for imagery analysis, feature extraction, and spatial reasoning using AI agents or traditional computer vision techniques. Design and deploy cloud-based ML/GenAI workflows for training and inference (AWS/Azure mandatory). Implement model tracking/versioning with MLflow or similar tools. Automate geospatial workflows with Python and integrate generative capabilities for GIS tasks. Collaborate on agentic AI systems for dynamic geospatial decision-making. Must-Have Skills: Programming: Python, Geospatial libraries (GDAL, Geopandas, Rasterio, shapely or ArcGIS API for Python). GenAI & Multi-Agent Systems: LangChain / Langgraph, LlamaIndex, RAG architectures. ML/DL: Computer vision with TensorFlow, PyTorch and Scikit-learn. Cloud: AWS/Azure for model deployment and training. ML Ops: MLflow or similar. Certifications (At least one required): Esri Technical Certification (e.g., ArcGIS API for Python - Associate) Google Cloud Professional Machine Learning Engineer Microsoft Certified: Azure AI Engineer Associate AWS Certified Machine Learning ? Specialty Nice-to-Have: API frameworks (FastAPI/Flask), Docker for containerized services. LiDAR/Point Cloud processing for advanced spatial modeling. Portfolio showcasing GenAI-driven geospatial projects.