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Team Lead – Artificial Intelligence
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Team Lead – Artificial Intelligence

Ceinsys Tech -
Meghalaya

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Job details

Full-time

Qualifications

TensorFlow
CI/CD
Image processing
Azure
Law
Computer Science
Software deployment
Engineering
Microsoft Office
Master's degree
AWS
Presentation skills
Bachelor's degree
Machine learning
Continuous integration
OpenCV
Data science
AI
Communication skills
GIS
Analytics

Full job description

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]

Apply Now

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