Location:
Experience: 5+ years in AI/ML Domain
Employment Type: Full-Time
Qualification:
Bachelor’s degree in Engineering, Computer Science, AI, Data Science, or related field.
Master’s / Postgraduate qualification in AI/ML/Data Science or related domain preferred.
Experience:
- Minimum 5+ years of experience in the AI/ML domain.
- Minimum 2+ years of experience in a technical leadership or Team Lead role.
- Proven expertise in designing and delivering production-grade AI solutions and enterprise-scale AI systems.
Requirements:
- Lead and mentor a team of AI engineers by providing technical direction, project guidance, and career development support.
- Understand complex business challenges across GIS, AEC, water, power, and related domains, and translate them into scalable AI-driven solutions.
- Define and drive end-to-end AI solution architecture including data pipelines, model development, training, deployment, and monitoring.
- Lead development of AI models focused on computer vision, OCR, image/video analytics, point cloud processing, and LLM-based applications.
- Establish and enforce best practices for data annotation, dataset preparation, model training, evaluation, validation, and continuous optimization.
- Drive innovation and adoption of advanced AI/ML technologies including Generative AI, LLMs, multimodal AI, and vision intelligence frameworks.
- Collaborate with cross-functional teams including product, GIS, engineering, domain experts, and business stakeholders for successful solution delivery.
- Ensure scalability, reliability, performance optimization, and production readiness of deployed AI systems.
- Define and implement MLOps strategies including CI/CD for ML pipelines, model versioning, retraining, monitoring, and lifecycle management.
- Contribute to pre-sales activities, technical proposals, architecture discussions, and AI-driven solution design initiatives.
- Guide the team in solving complex technical challenges involving AI model performance, inference optimization, and large-scale deployments.
- Drive AI governance, model explainability, security, and responsible AI practices across projects.
- Maintain technical documentation, architecture standards, reusable AI frameworks, and knowledge-sharing practices.
- Mentor mid-level AI engineers and build strong technical capabilities within the team.
- Strong communication, stakeholder management, collaboration, and presentation skills.
- Proficiency in MS Office and enterprise collaboration tools.
Key Performance Indicators:
- Accuracy, precision, recall, F1-score, and overall performance of deployed AI models.
- Successful delivery of AI solutions aligned with business requirements, timelines, and quality standards.
- Improvement in model scalability, inference efficiency, and production performance.
- Reduction in production issues, false predictions, and model drift through continuous optimization.
- Team productivity, capability development, and retention.
- Adoption and effectiveness of ML Ops practices, automation pipelines, and AI lifecycle management.
- Innovation and implementation of advanced AI techniques improving operational and business outcomes.
- Successful deployment of reliable, scalable, and production-grade AI systems across multiple domains.
Skills & Competencies:
- Strong expertise in Artificial Intelligence and Machine Learning with focus on Computer Vision, OCR, and Large Language Models (LLMs).
- Hands-on experience with image processing, video analytics, point cloud processing, and geospatial AI workflows.
- Proficiency in AI/ML frameworks such as TensorFlow, PyTorch, OpenCV, and related AI libraries.
- Experience with LLMs, prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and custom model development.
- Strong understanding of data annotation, dataset engineering, model evaluation, and validation methodologies.
- Experience with MLOps tools, model deployment pipelines, monitoring systems, and ML lifecycle management.
- Familiarity with cloud platforms such as AWS, Azure, or GCP for AI workloads and scalable infrastructure deployment.
- Exposure to GIS, geospatial intelligence, AEC, water, or power domain applications is highly preferred.
- Strong system architecture, analytical thinking, debugging, and problem-solving capabilities.
- Ability to define AI technical roadmaps aligned with business and product strategies.
- Leadership capabilities to mentor engineers, drive innovation, and manage cross-functional technical initiatives.
- Excellent communication, collaboration, presentation, and stakeholder management skills.
Send your CVs to [email protected]
