Lead Computer Vision Engineer
- Compensation
- Not disclosed
- Location
- Hyderabad
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Bachelor's/Master's degree in Computer Science, AI/ML, Data Science, or a related field.
- 5+ years of hands-on experience in machine learning and deep learning.
- Strong proficiency in Python and ML libraries (TensorFlow/PyTorch, Scikit-Learn, OpenCV, Hugging Face, etc. ).
- Proven experience deploying AI solutions into production environments (cloud or on-premises).
- Strong understanding of MLOps, CI/CD for ML, containerization (Docker), and orchestration (Kubernetes preferred).
- Experience working with structured & unstructured data (images, video, sensor data, and text).
- Hands-on experience with data pipelines, ETL processes, and real-time inference systems.
- Strong analytical and debugging skills with a track record of shipping stable solutions.
- Understanding of distributed computing frameworks (Spark or similar) is an advantage.
Full description
We are seeking an experienced Lead Computer Vision Engineer to lead the design, development, and deployment of cutting-edge AI and machine learning solutions. The ideal candidate will have strong hands-on expertise in Python, machine learning, and deep learning, along with experience in building scalable production-ready AI systems. This role requires a blend of technical excellence, innovation, leadership, and mentorship, ensuring high-impact delivery aligned to business needs. You will drive experimentation, improve model development pipelines, optimize data workflows, and build reusable AI components. The role involves identifying risks early, ensuring high-quality execution, and contributing to AI capability building across the organization.
The core responsibilities for the job include the following:
Computer Vision Development:
- Architect, develop, and deploy scalable AI/ML models for production with high performance and reliability.
- Build at least two innovative AI solutions aligned with business priorities annually.
- Ensure production-grade AI system delivery with < 5 bugs and strong reliability metrics.
- Improve end-to-end model development pipeline efficiency by at least 20%.
- Develop reusable machine learning modules, tools, and automation frameworks to reduce development complexity.
Data Engineering & Pipeline Optimization:
- Improve data preparation and annotation pipeline efficiency by 50%.
- Collaborate with data engineering teams to design efficient data pipelines and automated preprocessing workflows.
- Work with large datasets, perform data wrangling, feature engineering, and optimize training/validation pipelines.
Innovation & Solution Design:
- Lead research initiatives to design AI-first solutions for new business use cases.
- Evaluate new ML frameworks, model architectures, and tools to continuously enhance system performance.
- Conduct PoCs, benchmark results, and transition validated solutions into scalable production systems.
Quality & Risk Management:
- Identify potential project risks early and mitigate them, reducing unforeseen delivery issues by 99%.
- Enforce best practices in model evaluation, versioning, MLOps, and monitoring for long-term stability.
- Ensure model security, fairness, and compliance standards.
Team Leadership & Mentorship:
- Mentor and guide at least 2+ junior AI engineers to build skill depth and readiness for advanced roles.
- Provide technical leadership on architecture design, experimentation strategy, and performance tuning.
- Help the team resolve primary business dependencies and technical blockers efficiently.