About this role
The Machine Learning Engineer will drive and participate in the ideation, development, and execution of Proof of Concepts and AI-related projects. The role involves developing and implementing machine learning models, algorithms, and data-driven solutions to solve complex business problems. The engineer will collaborate cross-functionally with engineering, product management, and other relevant teams to integrate data-driven functionalities into products. The technical environment includes working with Large Language Models (LLMs), RAG applications, LangChain, Azure cloud services, Docker, and Kubernetes. The candidate must be possess a strong understanding of ability to build and maintain scalable, production-ready ML/AI systems using MLOps best practices.
Skills & technologies
Must have
- Python
- LLM
- RAG
- Embedding Models
- Semantic Search
- Prompt Engineering
- LangChain
- Azure
- Azure DevOps
- CI/CD
- Docker
- Kubernetes
- REST API
- Microservices
- MLOps
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What project we have for you
Are you a skilled Machine Learning engineer with a passion for Computer vision, NLP or Generative AI? Do you have a knack for understanding both the technical intricacies and the business implications of data-driven solutions? If so, we have an exciting opportunity for you to join our team as machine Learning Engineer
What you will do
- Drive/Participate the ideation, development, and execution of POCs and AI related project
- Develop and implement machine learning models, algorithms, and data-driven solutions to address complex business problems
- Collaborate cross-functionally with engineering, product management, and other relevant teams to integrate data-driven functionalities into our products.
What you need for this
Senior-level experience in Data Science, Machine Learning and AI engineering.
Strong programming skills in Python
Strong understanding of Large Language Models (LLMs), including architectures, capabilities, limitations and practical use cases.
Experience working with embedding models and semantic search techniques.
Hands-on experience designing and building Retrieval-Augmented Generation (RAG) applications.
Knowledge of prompt engineering and LLM orchestration frameworks (e.g. LangChain or similar tools).
Experience integrating LLMs through APIs and deploying AI-powered applications.
Understanding of AI evaluation methodologies, including LLM performance evaluation and monitoring.
Experience with Azure cloud services, particularly services relevant to AI and data workloads.
Practical experience with Azure DevOps, including CI/CD pipelines and repository management.
Experience containerising applications using Docker.
Experience deploying and managing containerised workloads with Kubernetes.
Familiarity with MLOps best practices, including model versioning, deployment and monitoring.
Experience building and maintaining scalable and production-ready ML/AI systems.
Knowledge of REST APIs and microservice architectures.
Understanding of software engineering best practices, including testing, code reviews and clean code principles.
Strong problem-solving and communication skills, with the ability to collaborate effectively with cross-functional teams.
