Description

Key Responsibilities

Design and develop AI/ML and agentic AI solutions for automation and decisioning use cases.
Build and maintain end-to-end ML pipelines for training, deployment, and monitoring.
Implement AI/ML solutions using GCP services such as Vertex AI, BigQuery ML, and Dataflow.
Develop and integrate agent-based or multi-step AI workflows with enterprise applications.
Build scalable data pipelines supporting batch and real-time processing.
Ensure model performance, reliability, and continuous optimization in production.
Collaborate with architecture and engineering teams to align AI solutions with platform design.
Implement MLOps practices including CI/CD, versioning, governance, and monitoring.

Required Skills

Experience
5–10 years of experience in AI/ML, data engineering, or cloud engineering.
Programming & Machine Learning
Strong programming skills in Python.
Experience with ML frameworks:
TensorFlow
PyTorch
Scikit-learn
GCP AI/ML Services

Hands-on experience with GCP AI/ML services:
Vertex AI
BigQuery ML
Dataflow
Agentic AI & GenAI
Experience with agentic AI, GenAI, and LLM-based workflows:
Multi-step reasoning
Orchestration
Agents
ML Engineering
Experience building ML pipelines.
Experience deploying models in production.
Architecture & Integration
Familiarity with microservices architecture.
Experience with API integration.
Containerization & DevOps

Experience with:
Kubernetes
Docker
CI/CD tools
Data Engineering
Experience building scalable data pipelines supporting batch and real-time processing.
MLOps

Experience with:
CI/CD
Versioning
Governance
Monitoring

Preferred Qualifications

Experience implementing:
RAG
Prompt orchestration
AI workflow automation
Experience in large-scale data and modernization programs.
Knowledge of Revenue Cycle Management financial systems (nice-to-have).
Relevant cloud or ML certifications (optional)

Key Skills

Education

Bachelor's degree