Senior Software Engineer III – AI

Carelon Global Solutions Philippines

AI Engineer optimizing machine learning models and data pipelines for healthcare at Carelon Global Solutions. Collaborating with cross-functional teams to innovate AI solutions for operational processes.

Posted 5/27/2026full-timeBengaluru • 🇮🇳 IndiaSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoGoogle Cloud PlatformJenkinsKubernetesMicroservicesNumpyPandasPythonPyTorchRustScikit-LearnSparkSQLTensorflowTerraform

About the role

Key responsibilities & impact

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.
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.
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.

Requirements

What you’ll need

Minimum education: Bachelor’s degree in any Engineering Stream
Specialized training, certifications, and/or other special requirements: Nice to have
Minimum relevant experience - 5+ years in AI Engineering
Preferred total experience – 8 years
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.

Benefits

Comp & perks

Extensive focus on learning and development
An inspiring culture built on innovation, creativity, and freedom.
Holistic well-being
Comprehensive range of rewards and recognitions
Competitive health and medical insurance coverage
Best-in-class amenities and workspaces
Policies designed with associates at the center.

ATS Keywords

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Hard Skills & Tools

PythonNumPyPandasscikit-learnPyTorchTensorFlowSparkKubernetesMLflowmodel quantization

Soft Skills

analytical thinkingcommunication skillsproject managementcollaborationmentoringproblem-solvingstakeholder engagementinnovationadaptabilityattention to detail