Specialist I - Data Science

9 - 12 Years
1 Opening
Bangalore

Role(s): GenAI Architect (Technical)
Role Location(s): Bangalore, India
Define and implement scalable, secure, and high-performance architectures for GenAI and Agentic AI solutions.
Design APIs, microservices, and distributed systems leveraging Python and FASTAPI.
Drive best practices in software architecture, design patterns, and scalability.
Development & Implementation
Develop AI-driven applications and services using Python and modern frameworks.
Build and integrate LLMs and AI agents into enterprise workflows.
Apply MLOps and DevOps practices for continuous integration, deployment, and monitoring of AI solutions.
Cloud & Infrastructure
Leverage AWS services (Lambda, ECS/EKS, S3, DynamoDB, SageMaker, etc.) to design and deploy cloud-native AI applications.
Ensure solutions are optimized for scalability, availability, performance, and cost-efficiency.
Implement security and compliance standards in cloud-based AI workloads.
Leadership & Collaboration
Work proactively as an individual contributor with ownership of design and delivery.
Lead and mentor team members when required, providing architectural guidance and technical leadership.
Collaborate with data scientists, engineers, and product managers to deliver impactful AI solutions.
Problem Solving & Innovation
Troubleshoot complex system and application issues with a solution-oriented approach.
Stay ahead of industry trends in GenAI, Agentic AI, and large-scale distributed systems.
Experiment with emerging frameworks (e.g., LangChain, LlamaIndex) to enhance AI capabilities.
Required Qualifications
12+ years of software development and architecture experience.
Strong expertise in Python development.
Proven experience with FASTAPI for building APIs and services.
Hands-on experience with AWS cloud services for development and deployment.
Experience in designing and developing applications for scale in distributed environments.
Expertise in Generative AI and Agentic AI (LLMs, AI agents, orchestration frameworks).
Excellent problem-solving and troubleshooting skills.
Ability to work independently and lead teams when necessary.
Preferred Qualifications
Experience with containerization and orchestration (Docker, Kubernetes).
Familiarity with event-driven and microservices architectures.
Good to have - Knowledge of MLOps frameworks (Kubeflow, MLflow, SageMaker, Vertex AI).
Understanding of data privacy, governance, and compliance in AI systems.

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