Technical Skills:
Advanced proficiency in Python
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.
DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations.
LLM Infrastructure & Deployment: Knowledge 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 TorchServe 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: Experience 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, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP.
Experience in creating LLD for the provided architecture.
Experience working in microservices based architecture.
Experience working in Observability.
Professional Competencies:
Strong analytical thinking with ability to solve complex challenges
Excellent communication skills for presenting technical findings to diverse audiences
Experience translating business requirements into data science solutions
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
Responsibilities
Technical Skills:
Advanced proficiency in Python
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.
DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations.
LLM Infrastructure & Deployment: Knowledge 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 TorchServe 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: Experience 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, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP.
Experience in creating LLD for the provided architecture.
Experience working in microservices based architecture.
Experience working in Observability.
Professional Competencies:
Strong analytical thinking with ability to solve complex challenges
Excellent communication skills for presenting technical findings to diverse audiences
Experience translating business requirements into data science solutions
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
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
