AI Developer

Job summary

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.

Job details

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.

What you’ll do

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.

Listed skills

• Python · Preferred
• SQL · Preferred
• REST APIs · Preferred
• Git · Preferred
• Docker · Preferred
• Kubernetes · Preferred
• Claude · Preferred
• prompt engineering · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• PySpark
• SQL
• REST APIs
• FastAPI
• Git
• Docker
• Kubernetes
• Apache Airflow
• MLflow
• Claude
• Prompt Engineering
• Retrieval-Augmented Generation
• Vector Databases
• MLOps
• LLMOps

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Finance & Accounting

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