Location: Chennai/Bangalore/Pune/Hyderabad
Design and review machine learning architecture pipelines end-to-end
Architect scalable models using tools like TensorFlow, PyTorch, Scikit-learn
Drive data strategy, feature stores, model lifecycle (training, deployment, monitoring)
Collaborate with data engineering and product teams to align technical goals
Define standards for MLOps, model governance, and responsible AI
8+ years in ML/DL, data science, or AI-related roles
Expertise in Python, ML frameworks, distributed computing (Spark, Dask)
Cloud-native ML deployment on AWS SageMaker, Azure ML, GCP Vertex AI
Knowledge of Kubeflow, MLFlow, Airflow, Docker/K8s for ML pipelines
Strong grasp of ML lifecycle management, monitoring, and retraining
Key Responsibilities
Own the technical design for backend systems built on Java / Spring Boot
Create solution blueprints, architecture documents, and design patterns
Guide teams on microservices, containerization, API design, and DevOps
Conduct code reviews, architecture assessments, and risk mitigation
Collaborate with product and infrastructure teams for seamless delivery
Proficiency in Java 11+/Spring Boot, RESTful APIs, ORM frameworks
Experience with Kubernetes, Docker, messaging systems (Kafka, RabbitMQ)
Strong grasp of cloud-native development (AWS, Azure, GCP)
In-depth understanding of microservices, DDD, clean architecture
Performance tuning, scalability, and reliability design
Key Responsibilities
Analyze requirements and design technical solutions across platforms
Create architecture roadmaps for applications, integrations, and cloud infrastructure
Lead POCs, architecture evaluations, and tech selection
Align solutions with enterprise architecture standards and security best practices
Act as a technical bridge between stakeholders, developers, and leadership
Strong experience in application architecture, APIs, and system integration
Familiarity with cloud platforms (Azure, AWS, GCP)
Solid understanding of data architecture, DevOps practices, CI/CD pipelines
Experience with design patterns, scalability, and resilience planning
Great communication, documentation, and stakeholder management skills
Skills Required
- 8+ years in ML/DL, data science, or AI-related roles
- Expertise in Python
- Experience with TensorFlow, PyTorch, Scikit-learn
- Experience with distributed computing (Spark, Dask)
- Cloud-native ML deployment experience (AWS SageMaker, Azure ML, GCP Vertex AI)
- Familiarity with Kubeflow, MLflow, and Airflow
- Experience with Docker and Kubernetes for ML pipelines
- Experience owning backend technical design with Java 11+ and Spring Boot
- Proficiency in RESTful APIs and ORM frameworks
- Experience with messaging systems (Kafka, RabbitMQ)
- Strong cloud-native development experience (AWS, Azure, GCP)
- Deep knowledge of microservices, DDD, and clean architecture
- Experience with MLOps, model lifecycle management, monitoring, and retraining
- Experience creating solution blueprints, POCs, and architecture roadmaps
- Strong communication, documentation, and stakeholder management skills
What We Do
Indium is an AI-driven digital engineering company that helps enterprises build, scale, and innovate with cutting-edge technology. We specialize in custom solutions, ensuring every engagement is tailored to business needs with a relentless customer-first approach. Our expertise spans Generative AI, Product Engineering, Intelligent Automation, Data & AI, Quality Engineering, and Gaming, delivering high-impact solutions that drive real business impact. With 5000+ associates globally, we partner with Fortune 500, Global 2000, and leading technology firms across Financial Services, Healthcare, Manufacturing, Retail, and Technology—driving impact in North America, India, the UK, Singapore, Australia, and Japan to keep businesses ahead in an AI-first world.
