Interesting Job Opportunity: Carelon - Artificial Intelligence Engineer - Python/LLM

Carelon Global Solutions India; Bengaluru; Presencial; INR 1.200.000 - 1.800.000; Jornada completa

Descripción de la vacante

Carelon Global Solutions India seeks an experienced AI Engineer to design and optimize production-grade ML systems in Bengaluru. You will build microservices, manage data pipelines, and orchestrate LLM inference at scale.

The role requires strong Python, ML frameworks, and cloud skills, plus hands-on experience with Terraform, Kubernetes, and MLflow for reproducible experimentation. Collaboration with data scientists and product teams is essential.

Formación

• Minimum education: Bachelor's degree in any Engineering Stream.
• Minimum relevant experience: 6+ years in AI Engineering.
• Nice to have specialized training or certifications.
• Preferred education: Computer-related field.

Responsabilidades

• Develop and maintain microservice architecture and API management for AI solutions.
• Collaborate with data scientists and product managers to acquire, process, and manage data for AI/ML model integration and optimization.
• Design scalable 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 Terraform, CloudFormation, and Pulumi for infrastructure management.
• Create and manage CI/CD pipelines using Git-based platforms (GitHub Actions, Jenkins). Learn more.
• Operate Kubernetes with advanced configurations for scalable ML workloads.
• Design and build scalable LLM inference architectures with GPU memory optimization and quantization.
• Engage in prompt engineering and fine-tuning of LLMs for semantic retrieval and chatbot development.
• Document model architectures and experiments using MLflow or DVC.
• Research LLM optimization techniques like quantization and distillation for efficiency.
• Collaborate with stakeholders on NLP solutions including text classification and sentiment analysis.
• Design and execute rigorous A/B tests for ML models.
• Stay current with AI tech trends and integrate new methodologies.
• Contribute to healthcare AI solutions leveraging domain knowledge.

Conocimientos

Python

LLM frameworks

Prompt engineering

Spark big data

Git MLflow

Kubernetes

Terraform & IaC

Educación

Bachelor's degree in Engineering

Herramientas

Hugging Face Transformers

LangChain

TorchServe

TF Serving

vLLM

FastAPI

Descripción del empleo

Job Responsibility

• 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, CloudFormation, and Pulumi for efficient infrastructure management.
• Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub 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 tools like MLflow or DVC.
• Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs.
• Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling.
• Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions.
• Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function.
• Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment.

Job Responsibility

• 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, CloudFormation, and Pulumi for efficient infrastructure management.
• Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub 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 tools like MLflow or DVC.
• Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs.
• Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling.
• Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions.
• Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function.
• Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment.

Qualification

• Minimum education : Bachelors degree in any Engineering Stream
• Specialized training, certifications, and/or other special requirements : Nice to have
• Preferred education : Computer :
• Minimum relevant experience - 6+ years in AI Engineering

Skills And Competencies

Technical Skills:

• Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow)
• Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) 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.
• 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 : 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 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 : 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, 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.

Domain Expertise

• Strong mathematical foundation in statistics, probability, linear algebra, and optimization
• 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

(ref:hirist.tech)