Senior Ai Engineer

Deepsource Technologies

Design, develop, deploy, and maintain production-grade AI and machine learning systems end to end.

Build and optimize LLM-powered applications, including RAG pipelines, prompt workflows, agent-based systems, and multimodal AI use cases.

Develop intelligent workflows using tool-calling, orchestration frameworks, and contextual reasoning patterns.

Fine-tune, evaluate, and operationalize machine learning and foundation models for enterprise use cases.

Build and manage MLOps pipelines covering training, evaluation, model registration, deployment, monitoring, and retraining.

Implement CI/CD pipelines for ML and AI workflows to support automated testing, release management, and controlled deployments.

Establish model monitoring frameworks for drift detection, feature attribution, inference quality, and performance tracking.

Ensure reproducibility, reliability, and version control across AI/ML environments.

Architect scalable AI/ML platforms using modern compute, storage, orchestration, monitoring, and search services.

Build repeatable environments using Infrastructure as Code.

Support secure, high-availability, and cost-efficient deployment models across development, staging, and production environments.

Design scalable inference and serving patterns for variable workloads.

Build and maintain automated data pipelines, ETL/ELT workflows, and data processing frameworks for AI/ML consumption.

Ensure data quality, lineage, governance, and versioning to support dependable model training and inference.

Work with structured and unstructured datasets across data lakes, data warehouses, and operational systems.

Deliver analytics-ready datasets to downstream systems and applications.

Lead multiple AI initiatives in parallel, including planning, execution, and coordination with internal teams and stakeholders.

Work closely with product, engineering, data, and business teams to deliver production-ready AI capabilities.

Contribute to architecture decisions, technical documentation, best practices, and engineering standards.

Support knowledge sharing, technical leadership, and continuous improvement across the AI function.