Senior AI ML Developer

Position Description

Founded in 1976, CGI is among the largest independent IT and business consulting services firms in the world. With 94,000 consultants and professionals across the globe, CGI delivers an end-to-end portfolio of capabilities, from strategic IT and business consulting to systems integration, managed IT and business process services and intellectual property solutions. CGI works with clients through a local relationship model complemented by a global delivery network that helps clients digitally transform their organizations and accelerate results. CGI Fiscal 2024 reported revenue is CA$14.68 billion and CGI shares are listed on the TSX (GIB.A) and the NYSE (GIB). Learn more at cgi.com.

Job Title: Senior AI ML Developer

Position: Senior Software Engineer

Experience: 8-12 Years

Category: Software Development/ Engineering

Main location: Pan India

Position ID: J0726-2253

Employment Type: Full Time

Education Qualification: Bachelor’s degree in computer science or related field or higher with minimum 3 years of relevant experience.

Position Description: CGI is seeking a talented Senior AI/ML Engineer with expertise in Machine Learning, Generative AI, Retrieval Augmented Generation (RAG), Python, and AWS Cloud Services to design, develop, and deploy enterprise scale AI solutions. The candidate will be responsible for building intelligent AI applications, developing ML models, implementing RAG based architectures, optimizing AI pipelines, and deploying scalable solutions on AWS.

The ideal candidate should have strong experience in AI/ML development, Generative AI technologies, cloud native architectures, and MLOps best practices.

Your future duties and responsibilities

AI/ML Solution Development

• Develop and deploy Machine Learning and Deep Learning models for business use cases.
• Design and implement end to end AI solutions using Python.
• Build scalable inference and prediction services using modern AI frameworks.
• Develop NLP solutions for text analytics, information extraction, and conversational AI.
• Integrate AI services with enterprise applications and external systems.

Generative AI & RAG

• Design and implement Retrieval Augmented Generation (RAG) solutions using LLMs.
• Build semantic search and knowledge retrieval systems using vector databases.
• Develop document ingestion, chunking, embedding, indexing, and retrieval pipelines.
• Integrate Large Language Models (OpenAI, Claude, Llama, Bedrock Models) with enterprise applications.
• Implement prompt engineering, response optimization, and AI guardrails.
• Evaluate and optimize GenAI applications for accuracy, latency, and cost.

Machine Learning Engineering

• Develop supervised and unsupervised learning models.
• Perform feature engineering, model training, validation, and performance tuning.
• Implement ML pipelines for automated model deployment and monitoring.
• Build recommendation systems, classification, forecasting, and predictive analytics solutions.
• Apply MLOps practices for model lifecycle management.

AWS & Cloud Services

• Develop and deploy AI solutions on AWS cloud infrastructure.
• Work with Amazon SageMaker for model training, deployment, and monitoring.
• Implement AI applications using AWS Bedrock and related Generative AI services.
• Utilize Amazon S3, Lambda, ECS, ECR, API Gateway, and RDS.
• Design scalable and secure cloud native AI architectures.
• Optimize cloud infrastructure for performance, reliability, and cost efficiency.

MLOps & DevOps

• Build CI/CD pipelines for AI and ML model deployments.
• Containerize applications using Docker and deploy using Kubernetes/ECS.
• Implement automated testing, monitoring, and model governance.
• Manage version control and deployment workflows using GitHub Actions or Jenkins.
• Establish model observability and performance monitoring frameworks.

Data Engineering & Processing

• Develop ETL and data preprocessing pipelines for structured and unstructured data.
• Work with large scale datasets from multiple enterprise data sources.
• Implement vector stores and embedding frameworks for RAG applications.
• Ensure data quality, governance, and security compliance.

System Reliability & Optimization

• Monitor AI applications and model performance in production.
• Troubleshoot model drift, performance degradation, and system bottlenecks.
• Optimize retri…