Staff Engineer - Machine Learning Engineer

AiDash; Bengaluru; On-site; INR 20,00,000 - 30,00,000; Full time

Job summary

A leading AI technology firm in Bengaluru seeks a seasoned Staff MLE to architect high-performing ML systems that transform satellite and aerial imagery into actionable insights. You will lead from model deployment to infrastructure design while collaborating with data science and engineering teams. The role demands extensive experience in machine learning, particularly with AWS services, and a strong foundation in Python programming.

Qualifications

• 5+ years of experience in machine learning engineering.
• Strong hands-on experience with AWS ML services.
• Expert-level Python programming with ML libraries.

Responsibilities

• Design, build, and maintain end-to-end ML pipelines.
• Deploy and scale ML models in production on AWS.
• Implement MLflow for experiment tracking and model management.

Skills

Machine learning engineering

AWS services (SageMaker, Lambda, ECS/EKS)

Image segmentation

Computer vision techniques

Python programming

Model monitoring systems

Tools

MLflow

Docker

Kubernetes

TensorFlow

PyTorch

Job description

About AiDASH

AiDASH is an enterprise AI company and the leading provider of vegetation risk intelligence for electric utilities. Powered by proprietary VegetationAI™ technology, AiDASH delivers a unified remote grid inspection and monitoring platform that uses a SatelliteFirst approach to identify and address vegetation and other threats to the grid. With a prevention-first strategy to mitigate wildfire risk and minimize storm impacts, AiDASH helps more than 140 utilities reduce costs, improve reliability, and lower liability across their networks. AiDASH exists to safeguard critical utility infrastructure and secure the future of humanAIty™.

The Role

We’re looking for a seasoned Staff MLE to shape and scale the backbone of our production ML ecosystem. In this role, you will architect high-performing ML systems that power our geospatial intelligence platform, transforming large-scale satellite and aerial imagery into actionable insights. You’ll lead end-to-end ownership—from model deployment and MLOps to infrastructure design—while partnering closely with data science, platform engineering, and product teams to deliver reliable, scalable, and cost-efficient ML solutions.

How you'll make an impact

• Design, build, and maintain end-to-end ML pipelines for batch processing of satellite and aerial imagery.
• Deploy and scale ML models in production on AWS infrastructure, leveraging services like SageMaker, Bedrock, or custom-built solutions.
• Implement MLflow for experiment tracking, model versioning, and model registry management.
• Architect batch inference systems optimized for throughput and cost-efficiency.
• Work with geospatial data formats and coordinate reference systems.
• Collaborate with data scientists to transition models from research to production.
• Partner with platform engineering to build scalable compute, GPU clusters, and storage layers.
• Implement comprehensive model monitoring systems to track performance degradation and data drift.
• Design and execute canary deployments and A/B testing frameworks for safe model rollouts.
• Build active learning pipelines to continuously improve model performance with minimal labeling effort.
• Establish model evaluation frameworks and performance benchmarking processes.
• Create alerting and observability systems for production ML workloads.

Technical Leadership

• Mentor ML engineers and data scientists on best practices for production ML.
• Drive technical decision-making on ML infrastructure and tooling.
• Collaborate with platform and data engineering teams to optimize the ML stack.
• Establish engineering standards and contribute to architectural roadmaps.

What we’re looking for:

• 5+ years of experience in machine learning engineering with 2+ years in a senior or lead capacity.
• Proven track record deploying and maintaining ML systems in production using AWS services (SageMaker, Lambda, ECS/EKS, S3, etc.).
• Strong hands-on experience with tools like MLflow, WandB, or similar for experiment tracking and model management.
• Deep expertise in image segmentation and computer vision techniques using frameworks like PyTorch or TensorFlow.
• Production experience with ensemble models (xgboost, lightgbm, RF).
• Experience implementing model monitoring, drift detection, and alerting systems.
• Hands-on experience with canary deployments, A/B testing, and Shadow deployments for ML models.
• Knowledge of active learning strategies and human-in-the-loop ML systems.
• Strong understanding of model evaluation metrics, bias detection, and performance analysis.
• Expert-level Python programming with ML libraries (scikit-learn, PyTorch/TensorFlow, NumPy, pandas, etc.).
• Experience with distributed batch processing frameworks (Airflow, Step Functions, Argo Workflows, Spark, Dask, Ray, or similar).
• Proficiency with AWS ML ecosystem and infrastructure-as-code (Terraform, CloudFormation).
• Hands-on experience with dataset versioning tools such as DVC, LakeFS, Delta Lake, Quilt, or Pachyderm.
• Strong software engineering fundamentals: unit/integration testing, CI/CD, version control, observability, design patterns.
• Experience with containerization (Docker, Kubernetes) for model deployment.
• Good to have experience with ML Orchestration tools like Kubeflow, Vertex AI, etc.
• Nice to have experience with GPUs: scheduling GPU jobs, optimizing GPU performance, memory profiling.

We are proud to be an equal-opportunity employer. We are committed to embracing diversity and inclusion in our hiring practices, and we promote a work environment where everyone, from any race, color, religion, sex, sexual orientation, gender identity, or national origin, can do their best work.

We are committed to providing an inclusive and accessible interview experience for all candidates. Please let us know if you require any accommodation during the interview process, and we will make every effort to meet your needs.