Exp : 5 - 10 Years

Mode of work : Hybrid Model

Work Location : Bangalore

Role Overview :

- Design and develop end-to-end agentic AI workflows and RAG pipelines using LangChain and LangFlow.

- Build and maintain production-grade Python services and microservices exposing AI capabilities through REST APIs and event-driven interfaces.

- Develop reusable Python libraries and utilities that accelerate AI pipeline development, data transformations, and integration workflows.

- Build GenAI-powered financial document parsing solutions for extracting and analyzing accounting statements for audit and business insights.

- Develop data orchestration pipelines with master data propagation, quality validation, and freshness checks across downstream systems.

- Design conversational AI chatbots and self-service interfaces for internal users to query enterprise data, reports, and business processes.

- Create embeddable UI components that integrate AI capabilities into existing enterprise platforms.

- Implement enterprise integrations including SSO, Slack workflows, and LLM provider APIs across AWS Bedrock and related AWS AI services.

- Containerize and deploy Python-based AI services on Kubernetes with high availability and operational observability.

- Build, train, and run comparative analysis of ML models to select optimal approaches for business problems.

- Implement MLOps pipelines using Airflow and maintain model observability using Grafana, Arize, or similar tools.

- Write clean, well-documented, and testable Python code following unit testing, CI/CD, and code review standards.

- Provide ongoing support and maintenance for deployed AI models, pipelines, and services in production.

- Stay current with emerging trends in Generative AI and agentic frameworks to continuously enhance solution quality.

Roles & Responsibilities :

- Experience working with GenAI solutions using LLMs.

- Proficiency with LangChain and LangFlow frameworks for building and testing Generative AI workflows and agentic pipelines.

- Proficiency in Python for AI/ML development, microservice development, and automation.

- Proficiency in core Python libraries such as pandas and NumPy.

- Experience with RAG pipeline design, LLM integration, and vector store management.

- Experience with ML model building and comparative analysis.

- Exposure to AI/ML frameworks such as TensorFlow, PyTorch, scikit-learn, etc.

- Experience with Hugging Face.

- Understanding of NLP techniques such as text summarization, sentiment analysis, and Named Entity Recognition (NER).

- Hands-on experience building and deploying REST APIs and event-driven Python services.

- Experience with ML Ops tooling, particularly Airflow.

- Experience with observability tools such as Grafana, Arize, or similar.

- Knowledge of enterprise AI infrastructure and best practices on AWS.

- Familiarity with Kubernetes for containerized workload deployment and management.

- Experience with enterprise integrations including SSO, Slack workflows, and embeddable UI development.

- Experience with financial document parsing, data orchestration, and conversational AI interface development.

- Experience designing, developing, deploying, and maintaining software in production environments.

- Experience working in a Scrum/Agile environment.

- A CS, Engineering, or related university degree is a must-have.

- Excellent written and verbal communication skills in English, with the ability to collaborate cross-functionally and present solutions to client stakeholders.