Location: Remote
Shift Timing: 1:30 PM – 10:30 PM IST
Type: Contract
Interview Process:
- Initial 15-minute HR Screening
- Followed by a Technical Evaluation with 1 or 2 Client Rounds
Background Verification (During Onboarding):
- Employment History Check
- PCC (Police Clearance Certificate / Criminal Record Check)
- Fluent English Communication (mandatory for collaboration with global teams)
Role Summary:
We are looking for a highly capable and agile Machine Learning Associate with a strong foundation in Computer Vision to support a cutting-edge AI initiative in pharmaceutical manufacturing automation. This role focuses on developing and integrating machine learning models within an AWS-native environment, with an emphasis on real-time anomaly detection using high-definition camera feeds on a drug manufacturing line.
Project Overview:
- Use Case: AI Computer Vision Model Development for Drug Manufacturing Line Clearance
- Infrastructure: Fully AWS-based environment
- ML Platform: AWS SageMaker
- Objective: Develop and deploy CV models to monitor live HD video feeds, identify anomalies, and compare real-time data against trained models of normal operations
- Responsibility: Adapt and align software models with real-world camera hardware using scalable, GPU-accelerated infrastructure
Technical Skills Required:
Core Expertise:
- Computer Vision – Senior/Expert level
- AWS SageMaker – Model training, deployment, optimization
- AWS Greengrass – Edge computing and device software integration
Supporting Skills:
- Experience working with NVIDIA Triton Inference Server for GPU-accelerated model inference
- Familiarity with model performance optimization and inference latency tuning
Key Responsibilities:
- Develop, train, and fine-tune computer vision models using AWS SageMaker
- Integrate ML models with real-time video processing from HD camera feeds
- Collaborate with engineering teams to align model output with hardware integration using AWS Greengrass
- Monitor model performance and accuracy using edge deployment and feedback loops
- Troubleshoot and resolve complex integration challenges between AI models and physical devices
- Contribute to continuous improvement through agile practices, documentation, and innovation
Nice to Have:
- Prior experience in life sciences, pharma, or manufacturing AI use cases
- Exposure to MLOps pipelines and CI/CD practices in model deployment
- Background in real-time or edge-based inference systems
Job Types: Full-time, Contractual / Temporary
Contract length: 12 months
Pay: ₹90,000.00 - ₹100,000.00 per month
Schedule:
- Day shift
- Monday to Friday
Experience:
- AWS green house: 3 years (Required)
- Machine learning: 7 years (Required)
Work Location: Remote