Original job description
Required Experience
10+ years of experience in Data Engineering, AI Application Development, Cloud Data Platforms, and production-grade software engineering.
Technical Skills
Strong proficiency in Python, PySpark, SQL, REST APIs, FastAPI, Git, Docker, Kubernetes, Apache Airflow, and MLflow.
Hands-on experience in developing AI-powered applications using Claude, prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, semantic search, and agentic AI architectures.
Expertise in implementing MLOps and LLMOps practices, including model lifecycle management, CI/CD pipelines, performance monitoring, and rollback mechanisms.
Key Responsibilities
AI Application Development: Design, develop, and deploy scalable AI-powered applications leveraging large language models (LLMs), RAG architectures, vector databases, semantic search, and agentic AI patterns.
MLOps and LLMOps: Implement and manage end-to-end MLOps and LLMOps capabilities, including model lifecycle management, automated CI/CD pipelines, experiment tracking, monitoring, and rollback strategies using tools such as MLflow.
Data Engineering: Design, build, and maintain robust end-to-end data engineering frameworks, including ETL/ELT processes, batch and streaming pipelines, data integration, data quality validation, and DataOps practices.
API Integration and Prototyping: Rapidly develop and validate prototypes using real-world client data, external APIs, and enterprise systems to demonstrate technical feasibility and business value.
Model and Application Evaluation: Evaluate and measure the quality, accuracy, reliability, and performance of ML and LLM-based solutions, implementing appropriate testing and validation frameworks.
Production Deployment and Support: Take end-to-end ownership of AI and data solutions following deployment, ensuring production readiness, operational stability, performance optimization, monitoring, and ongoing maintenance.
Cloud Data Platforms and Engineering: Develop and operate scalable, reliable data and AI solutions across cloud-based environments, following software engineering best practices and established architectural standards.
Security and Compliance: Work within Banking, Financial Services, and Insurance (BFSI) data environments, ensuring compliance with security and regulatory requirements, including identity and access management (IAM), role-based access control (RBAC), and data encryption.
Preferred Domain Experience
Experience working with BFSI clients, enterprise data platforms, and sensitive financial data.
Strong understanding of production-grade AI systems, secure data processing, and enterprise application deployment.
Proven ability to translate business requirements into scalable, reliable, and production-ready AI and data engineering solutions.
More about this job
Responsibilities
Design, build, and deploy production-ready AI applications and data engineering solutions, including LLM and RAG systems, data pipelines, APIs, and cloud-based platforms. Own solutions through deployment and ongoing operations, including evaluation, monitoring, performance optimization, security, compliance, and maintenance.
Requirements
Requires 10+ years of experience in data engineering, AI application development, cloud data platforms, and production-grade software engineering, with strong proficiency in the listed programming, data, and deployment tools. Candidates should have hands-on experience with LLM applications, RAG, agentic AI, MLOps/LLMOps, and secure enterprise data environments; BFSI experience is preferred.
Skills
- Python
- PySpark
- SQL
- REST APIs
- FastAPI
- Git
- Docker
- Kubernetes
- Apache Airflow
- MLflow
- Claude
- Prompt Engineering
- Retrieval-Augmented Generation
- Vector Databases
- MLOps
- LLMOps
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
- Finance & Accounting
Keywords
- Data Engineering
- AI Application Development
- Cloud Data Platforms
- Python
- PySpark
- SQL
- REST APIs
- FastAPI
- Git
- Docker
- Kubernetes
- Apache Airflow
- MLflow
- Claude
- Prompt Engineering
- Large Language Models
- Retrieval-Augmented Generation
- Vector Databases
- Semantic Search
- Agentic AI
- MLOps
- LLMOps
- CI/CD
- ETL/ELT
- Batch and Streaming Pipelines
- DataOps
- Cloud Computing
- BFSI
- Identity and Access Management
- Role-Based Access Control
