Artificial Intelligence Engineer

Location: Bengaluru

Job Role - AI Engineer
Location - Bengaluru/Gurgaon
Experience - 4 to 9 Years
Big 4 Company

Job Description:

Technical

Skills:

• Advanced proficiency in Python.

• Extensive experience with LLM frameworks (Hugging Face Transformers, Lang Chain) and prompt engineering techniques

• Experience with big data processing using Spark for large-scale data analytics

• Version control and experiment tracking using Git and MLflow

• Software Engineering & Development:
Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing.

• Dev Ops &

Infrastructure:

Experience with Infrastructure as Code (Terraform, Cloud Formation), CI/CD pipelines (Git Hub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations.

• LLM Infrastructure & Deployment:
Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management.

• MLOps & Deployment:
Utilization of containerization strategies for ML workloads, experience with model serving tools like Torch Serve or TF Serving, and automated model retraining.

• Cloud &

Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems.

• LLM Project

Experience:

Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security.

• General

Skills:

Python, SQL, knowledge of machine learning frameworks (Hugging Face, Tensor Flow, PyTorch), and experience with cloud platforms like AWS or GCP.

• Experience in creating LLD for the provided architecture.

• Experience working in microservices based architecture.

Domain Expertise:

• Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation

• Expertise in feature engineering, embedding optimization, and dimensionality reduction

• Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing

• Experience with RAG systems, vector databases, and semantic search implementation

• Proficiency in LLM optimization techniques including quantization and knowledge distillation

• Understanding of MLOps practices for model deployment and monitoring
Professional

Competencies:

• Strong analytical thinking with ability to solve complex ML challenges

• Excellent communication skills for presenting technical findings to diverse audiences

• Experience translating business requirements into data science solutions

• Project management skills for coordinating ML experiments and deployments

• Strong collaboration abilities for working with cross-functional teams

• Dedication to staying current with latest ML research and best practices

• Ability to mentor and share knowledge with team members

• Develop and maintain microservice architecture and API management solutions using REST and gRPC for seamless deployment of AI solutions.

• Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization.

• Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models.

• Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements.

• Familiarity with tools like Terraform, Cloud Formation, and Pulumi for efficient infrastructure management.

• Create and manage CI/CD pipelines using Git-based platforms (e.g., Git Hub Actions, Jenkins) to ensure streamlined development workflows.

• Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments.

• Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment.

• Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development.

• Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking…