AI Engineer
Posted Updated 3
views
Summary
An AI engineer who builds reusable model-training pipelines and production-grade AI microservices (ML, LLMs, agentic AI) with REST APIs, testing, and cloud scaling for client ecosystems. Core stack: Python and Java with Spring Boot, FastAPI, LangChain, plus containerization and CI/CD.
Job Summary (List Format):
- Build reusable pipelines for domain-specific model training using various post-training techniques.
- Design, develop, and deploy AI-powered microservices (including ML and agentic AI) with emphasis on modularity, scalability, and reusability.
- Transition AI models and workflows from proof-of-concept to production-grade solutions.
- Develop robust APIs for seamless integration of AI microservices within client ecosystems.
- Implement and execute comprehensive testing strategies (unit, integration, performance) to ensure quality and reliability of AI services.
- Optimize performance of AI microservices and infrastructure for latency, throughput, and cost efficiency.
- Collaborate with data science and infrastructure teams to create reusable pipelines for feature engineering, model optimization, and evaluation.
- Work cross-functionally with data scientists, MLOps engineers, product owners, and stakeholders to translate business requirements into technical specifications.
- Research and adopt emerging technologies (e.g., LLMs, frameworks) to enhance AI software development and data processing.
- Ensure all AI microservices comply with security, compliance, and ethical AI standards, contributing to best practices in AI engineering.
- Utilize expertise in model post-training techniques (e.g., SFT, Distillation, Pruning, RL).
- Develop and deploy RESTful APIs and microservices using Python, Java, and associated frameworks (Spring Boot, FastAPI, LangChain, etc.).
- Employ containerization technologies and CI/CD pipelines for efficient deployment and monitoring.
- Demonstrate experience in scaling machine learning models and utilizing cloud platforms (cloud certification preferred).
- Apply knowledge of ML, deep learning, neural network, and transformer architectures.
- Maintain good practices in data governance, quality, and security for AI/ML solutions.
- Communicate complex technical concepts effectively to both technical and non-technical audiences.
- Build reusable pipelines for domain-specific model training using various post-training techniques.
- Design, develop, and deploy AI-powered microservices (including ML and agentic AI) with emphasis on modularity, scalability, and reusability.
- Transition AI models and workflows from proof-of-concept to production-grade solutions.
- Develop robust APIs for seamless integration of AI microservices within client ecosystems.
- Implement and execute comprehensive testing strategies (unit, integration, performance) to ensure quality and reliability of AI services.
- Optimize performance of AI microservices and infrastructure for latency, throughput, and cost efficiency.
- Collaborate with data science and infrastructure teams to create reusable pipelines for feature engineering, model optimization, and evaluation.
- Work cross-functionally with data scientists, MLOps engineers, product owners, and stakeholders to translate business requirements into technical specifications.
- Research and adopt emerging technologies (e.g., LLMs, frameworks) to enhance AI software development and data processing.
- Ensure all AI microservices comply with security, compliance, and ethical AI standards, contributing to best practices in AI engineering.
- Utilize expertise in model post-training techniques (e.g., SFT, Distillation, Pruning, RL).
- Develop and deploy RESTful APIs and microservices using Python, Java, and associated frameworks (Spring Boot, FastAPI, LangChain, etc.).
- Employ containerization technologies and CI/CD pipelines for efficient deployment and monitoring.
- Demonstrate experience in scaling machine learning models and utilizing cloud platforms (cloud certification preferred).
- Apply knowledge of ML, deep learning, neural network, and transformer architectures.
- Maintain good practices in data governance, quality, and security for AI/ML solutions.
- Communicate complex technical concepts effectively to both technical and non-technical audiences.