AI/ML Specialist – Computer Vision

Role: AI/ML Specialist – Computer Vision

Type: Pune (flexibility for US team collaboration)

Position: Expert-Level

Location: Pune, India

Contact:

About Fluid Analytics:

Fluid Analytics is an award winning cleantech start-up, using cutting-edge technologies to help countries around the world tackle urban water pollution. The company's unique platform is deployed by cities to monitor the health of water infrastructure, the health of waterways (lakes, rivers, and coastlines), and public health. Fluid Analytics was recently awarded as the Global Top Innovator by the World Economic Forum, at this year's Annual Meeting in Davos, Switzerland.

Come join our team of passionate individuals, striving to make cities around the world water resilient.

We are looking for

We are seeking an expert-level Machine Learning leader to architect, scale, and own the end-to-end lifecycle of ML initiatives at Fluid Analytics. This role demands deep technical mastery in ML/AI, proven experience in building scalable systems, and the ability to translate cutting-edge research into impactful real-world solutions. You will play a pivotal role in shaping the company's ML/AI strategy, leading innovation, and growing a high-performing team.

Roles & Responsibilities:

• Advanced Model Development: Design, train, and fine-tune state-of-the-art deep learning models for computer vision applications, tackling complex problems in image recognition, object detection, and more.
• Define and drive the ML vision and strategy in alignment with company goals.
• Define and track KPIs for the ML/AI function.
• Lead and mentor a cross-functional team of ML engineers, data scientists, and annotation specialists within defined budgets.
• Performance Evaluation: Fine-tuning models for performance, including speed, accuracy, and resource usage
• Deployment: Deploying machine learning models to production environments and optimizing them for performance, scalability, and reliability.
• Management: Managing the tools and platforms needed to deploy and monitor models, including cloud services, containerization, and orchestration.
• Collaboration: Work closely with cross-functional teams, including product management and engineering, to integrate computer vision solutions into new and existing products.

What You'll Bring to Fluid Analytics:

We are seeking a talented Machine Learning Engineer with a strong focus on computer vision to join our team. In this role, you will be responsible for developing, training, and deploying machine learning models that solve complex vision-related tasks across diverse applications.

Key Responsibilities:

• Develop, train, and deploy machine learning models, with a focus on computer vision tasks like object detection, image segmentation, and image enhancement.
• Write clean, efficient, and scalable code in Python.
• Apply experience with key computer vision libraries like NumPy, Pandas, and OpenCV.
• Work with image processing techniques such as image enhancement, normalization, feature extraction (SIFT, SURF, HOG), and improving model performance.
• Implement deep learning frameworks (TensorFlow, PyTorch) and use transfer learning or fine-tune pre-trained models.
• Understand and apply CNN architectures like YOLO, Faster R-CNN, Mask R-CNN for object detection and segmentation.
• Utilize generative models (GANs) for tasks like image generation or style transfer.
• Work with data structures such as trees, graphs, and multidimensional arrays for processing and storing image data.
• Use your strong foundation in linear algebra, probability, and statistics to improve model accuracy and efficiency.
• Manage large volumes of image and video data using distributed storage solutions like HDFS, S3, or Google Cloud Storage.
• Deploy and manage machine learning models on cloud platforms (AWS, GCP, or Azure) using containerization (Docker) and orchestration tools (Kubernetes).
• Work closely with Founders, Product, and Engineering teams to integrate ML tasks into the Agile development process.
• Drive sprint planning & execution with measurable milestones.
• Enforce lean development principles (shipping to production every sprint).
• Maintain high code quality standards with code reviews, modular design, and TDD.
• Establish version control for models (Git/MLflow) and implement CI/CD pipelines.

Qualifications:

• Proven experience in computer vision and machine learning.
• Ability to tackle real-world problems and innovate on solutions.
• Strong analytical and problem-solving skills, with attention to model performance and accuracy.
• Experience in cloud-based machine learning model deployment is a plus.
• 5+ years of overall experience, with at least 4+ years in ML/AI and 4+ years in leadership roles.
• Proven expertise in designing, deploying, and scaling cloud-native ML/AI systems in production.
• Hands-on mastery of deep learning, predictive modeling, multimodal AI (image, video) and GenAI.
• Strong background in MLOps (CI/CD, monitoring, serverless workflows).
• Experience in data governance, compliance (ISO27001), and ethical AI standards.
• Track record of filing patents, publishing research, and driving industry thought leadership.
• Exceptional communication, stakeholder management, and cross-functional collaboration skills.

Please Note:

• This position is not for NLP or Big Data

Why Join Us?

• Drive ML innovation with direct impact on critical public utility systems.
• Own the strategic direction of ML/AI for the company.
• Opportunity to file patents, publish research, and represent the company at global forums.
• Build and scale a world-class ML team with strong backing from founders and leadership.
• A chance to work on cutting-edge, high-impact problems with global exposure.

Equal Employment Opportunity:

Fluid Analytics is dedicated to fostering a workplace that values diversity and inclusion. We are an equal opportunity employer, encouraging applications from candidates of diverse backgrounds.

Job Type: Full-time

Pay: ₹1,500, ₹2,200,000.00 per year

Benefits:

• Commuter assistance
• Flexible schedule
• Paid sick time
• Paid time off
• Provident Fund

Work Location: In person