Senior AI/ML Engineer

Jobtailor; Chennai District; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Jobtailor is seeking a senior ML Engineer to build and deploy production-grade computer vision models, focusing on object detection, visual embeddings, and robust image understanding for mobile and cloud deployments.

You will design scalable ML pipelines, optimize inference across edge and cloud, and collaborate with mobile/backend teams to integrate AI into production applications, while advancing the company’s AI strategy.

Qualifications

• 5+ years of experience building and deploying production machine learning systems.
• Strong expertise in computer vision, including object detection, image understanding, and visual embeddings.
• Experience training, fine-tuning, evaluating, and deploying deep learning models using real-world datasets.
• Hands-on experience with modern deep learning frameworks such as PyTorch or TensorFlow.
• Experience optimizing models for both cloud and edge/mobile deployment.
• Strong understanding of vector search, similarity retrieval, and embedding-based systems.
• Experience building scalable ML pipelines, model evaluation frameworks, and production inference services.
• Excellent Python development skills and the ability to work across multiple parts of the technology stack.
• Strong experience with AWS cloud services, including designing and deploying ML solutions using SageMaker, Lambda, S3 and related services.

Responsibilities

• Build and Improve Production AI Models.
• Design, train, fine-tune, and deploy state-of-the-art computer vision models for object detection, image understanding, and visual search.
• Improve model robustness across challenging real-world scenarios including varying lighting conditions, perspective changes, motion blur, occlusion, compression artifacts, and partial captures.
• Continuously evaluate and iterate on models using production feedback and newly collected data.
• Optimize AI for Mobile and Cloud.
• Optimize inference performance for both edge and cloud deployments, balancing accuracy, latency, memory usage, and operational cost.
• Improve mobile ML performance across iOS and Android while accounting for device constraints such as thermal throttling, battery usage, and hardware acceleration.
• Build scalable cloud inference services capable of supporting high-volume production workloads.
• Build a World-Class ML Platform.
• Design reproducible training pipelines, model versioning strategies, and automated evaluation workflows.
• Establish quality gates and validation processes to ensure models meet production standards before deployment.
• Improve CI/CD pipelines, artifact management, and promotion workflows across development, staging, and production environments.
• Own Data Quality and Evaluation.
• Develop data collection strategies that improve model performance and generalization.
• Create synthetic and augmented datasets to increase robustness across diverse operating conditions.
• Build automated benchmarking and evaluation pipelines with measurable performance metrics and regression testing.
• Improve Visual Search and Retrieval.
• Design and optimize image embedding and similarity search pipelines.
• Improve semantic matching, reranking, and retrieval quality for image-based search experiences.
• Evaluate new architectures and techniques that enhance accuracy and user experience.
• Collaborate Across Engineering Teams.
• Work closely with mobile and backend engineers to integrate AI models into production applications.
• Debug end-to-end ML systems, from training pipelines and inference services to client-side image preprocessing and post-processing.
• Contribute to technical architecture decisions and establish best practices for scalable AI development.
• Drive Innovation.
• Evaluate emerging AI technologies and identify opportunities to improve existing capabilities.
• Prototype new features in computer vision, multimodal AI, recommendation systems, and conversational AI.
• Help shape the long-term AI strategy and technical roadmap.

Skills

Computer vision

Object detection

Deep learning

PyTorch

TensorFlow

AWS SageMaker

Edge deployment

ML pipelines

Python

Vector search

Similarity retrieval

Tools

SageMaker

Lambda

S3

Job description

Responsibilities

• Build and Improve Production AI Models
• Design, train, fine‑tune, and deploy state‑of‑the‑art computer vision models for object detection, image understanding, and visual search
• Improve model robustness across challenging real‑world scenarios including varying lighting conditions, perspective changes, motion blur, occlusion, compression artifacts, and partial captures
• Continuously evaluate and iterate on models using production feedback and newly collected data
• Optimize AI for Mobile and Cloud
• Optimize inference performance for both edge and cloud deployments, balancing accuracy, latency, memory usage, and operational cost
• Improve mobile ML performance across iOS and Android while accounting for device constraints such as thermal throttling, battery usage, and hardware acceleration
• Build scalable cloud inference services capable of supporting high‑volume production workloads
• Build a World‑Class ML Platform
• Design reproducible training pipelines, model versioning strategies, and automated evaluation workflows
• Establish quality gates and validation processes to ensure models meet production standards before deployment
• Improve CI/CD pipelines, artifact management, and promotion workflows across development, staging, and production environments
• Own Data Quality and Evaluation
• Develop data collection strategies that improve model performance and generalization
• Create synthetic and augmented datasets to increase robustness across diverse operating conditions
• Build automated benchmarking and evaluation pipelines with measurable performance metrics and regression testing
• Improve Visual Search and Retrieval
• Design and optimize image embedding and similarity search pipelines
• Improve semantic matching, reranking, and retrieval quality for image‑based search experiences
• Evaluate new architectures and techniques that enhance accuracy and user experience
• Collaborate Across Engineering Teams
• Work closely with mobile and backend engineers to integrate AI models into production applications
• Debug end‑to‑end ML systems, from training pipelines and inference services to client‑side image preprocessing and post‑processing
• Contribute to technical architecture decisions and establish best practices for scalable AI development
• Drive Innovation
• Evaluate emerging AI technologies and identify opportunities to improve existing capabilities
• Prototype new features in computer vision, multimodal AI, recommendation systems, and conversational AI
• Help shape the long‑term AI strategy and technical roadmap

Requirements

• 5+ years of experience building and deploying production machine learning systems
• Strong expertise in computer vision, including object detection, image understanding, and visual embeddings
• Experience training, fine‑tuning, evaluating, and deploying deep learning models using real‑world datasets
• Hands‑on experience with modern deep learning frameworks such as PyTorch or TensorFlow
• Experience optimizing models for both cloud and edge/mobile deployment
• Strong understanding of vector search, similarity retrieval, and embedding‑based systems
• Experience building scalable ML pipelines, model evaluation frameworks, and production inference services
• Excellent Python development skills and the ability to work across multiple parts of the technology stack
• Strong experience with AWS cloud services, including designing and deploying ML solutions using SageMaker, Lambda, S3 and related services