Senior ML Engineer - Big Entities

About the job

Senior ML Engineer - Big Entities

Our client Big Entities is looking for Senior ML Engineer to work remotely.

Location and Timings

Location: DHA Phase 3 (Remote)

Timings: 10 AM - 7 PM (Fri - Sat Off days)

Role Summary

We are looking for a hands-on Computer Vision and ML Engineer with deep expertise in deep object detection and strong production delivery skills. You will own end-to-end detection systems, from dataset and training pipelines to optimized inference services, monitoring, and continuous improvement.

Key Responsibilities

• Design, train, debug, and improve state-of-the-art object detection models for real-world conditions
• Build robust training pipelines: datasets, augmentation, caching, versioning, and reproducible experiments
• Perform systematic error analysis and ablations to isolate failure modes (data vs model vs inference vs post-processing)
• Develop custom detection systems beyond standard training, including multi-stage pipelines, ensembles, and specialized post-processing
• Optimize inference for latency, throughput, and memory, including GPU acceleration and export toolchains
• Deliver production-grade services using Docker, Linux, CI/CD, and APIs (FastAPI and/or gRPC)
• Implement testing strategy across the pipeline (unit, integration, regression), including golden image test sets
• Set up monitoring and maintenance: logging, metrics dashboards, drift/performance tracking, retraining triggers
• Write clear technical documentation, architecture decisions, and trade-off analyses
• Read research papers and rapidly translate ideas into working prototypes and deployable components

Required Skills and Experience:

Python and ML Engineering

• Advanced Python engineering: clean architecture, packaging, typing, testing, profiling
• Strong PyTorch experience (must)
• TensorFlow optional
• Strong model debugging skills and disciplined experimentation
• Experiment tracking and reproducibility: W&B and/or MLflow, deterministic runs, seed control
• Config management: Hydra and/or OmegaConf
• Data pipelines: PyTorch Dataset/DataLoader, augmentation pipelines, caching
• Dataset versioning: DVC or equivalent

Computer Vision Fundamentals

• Strong CV fundamentals: preprocessing, geometry, photometric effects, distortions, camera models
• OpenCV expertise for classical CV and integration into modern ML pipelines
• Evaluation expertise: mAP, precision/recall, IoU, PR curves, calibration

Deep Object Detection Expertise

• Hands-on experience with modern detectors such as: YOLO (v5/v8/v9), Faster R-CNN, RetinaNet, EfficientDet, DETR variants
• Experience building advanced detection workflows:
• Multi-stage detection (proposal, refine, classify)
• Ensemble and stacking strategies
• Specialized post-processing tuned to domain constraints

Production ML and MLOps Delivery

• Model export and serving: ONNX export/runtime, plus at least one of TorchScript or TensorRT
• GPU inference optimization and performance tuning (batching, throughput, latency, memory)
• Deployment: Docker, Linux, CI/CD basics (GitHub Actions and/or GitLab CI)
• Service implementation: FastAPI and/or gRPC, model versioning, rollback strategy
• Monitoring and lifecycle: drift/performance monitoring, logging, dashboards, retraining triggers
• Testing: unit tests for preprocessing/post-processing, integration tests, regression sets, threshold stability tests

R&D Capability

• Ability to read papers and implement ideas quickly
• Strong debugging methodology, ablation design, and error analysis
• Clear technical writing and engineering decision-making

Nice-to-Have (Strong Bonuses)

• Engineering Drawings Domain
• Experience with engineering drawings and technical documents
• PDF vector vs raster workflows, line detection, symbol detection
• Table/diagram understanding, CAD-like concepts, annotation workflows
• OCR + vision hybrid systems (even if not OCR-first)
• Document and Diagram Vision Toolchain
• PyMuPDF and/or pdfplumber
• Image rasterization, coordinate transforms
• Handling noisy scans: skew/warp correction, deskewing

Broader CV Capabilities

• Instance segmentation: Mask R-CNN, YOLO-seg
• Keypoints, pose, landmark detection
• Tracking for video: ByteTrack, DeepSORT