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AIML Engineer
Job Location:
Monthly Salary:
Experience Required:
Posted:
27 September 2026 (Yesterday)
Posted:
Application Deadline:
25 December 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Summary
We are seeking a skilled AI/ML Engineer to join our IT Services this pivotal role the successful candidate will design develop and deploy advanced machine learning models and artificial intelligence solutions. This position is essential for driving innovation and delivering high-impact technical outcomes that leverage data science to solve complex business challenges.
Responsibilities
- Architect and implement scalable machine learning pipelines using tools like Pandas NumPy Spark and SQL.
- Containerize applications and manage orchestration environments with Docker and Kubernetes while maintaining version control via Git and CI/CD workflows.
- Develop and integrate RESTful APIs to support microservices architectures.
- Build and optimize vector search systems utilizing platforms such as Pinecone Weaviase and FAISS.
- Establish robust model monitoring protocols and automate the retraining processes to ensure system reliability.
- Leverage generative AI frameworks including Hugging Face LangChain LLMs and RAG architectures.
- Deploy and manage models on cloud platforms including Azure ML Azure OpenAI AWS SageMaker and GCP Vertex AI.
- Implement MLOps best practices using MLflow and Kubeflow to streamline the machine learning lifecycle.
Requirements
Requirements:
- Possess 4 to 6 years of professional experience in data engineering or machine learning roles.
- Demonstrate proficiency with data manipulation libraries such as Pandas and NumPy alongside big data processing with Spark and SQL.
- Have hands-on experience with containerization orchestration and DevOps tools like Docker Kubernetes Git and CI/CD.
- Show expertise in designing REST APIs and working within microservices ecosystems.
- Understand vector database technologies and similarity search mechanisms using Pinecone Weaviase or FAISS.
- Be capable of setting up automated model monitoring and retraining workflows.
- Possess strong knowledge of generative AI tools specifically Hugging Face LangChain Large Language Models and Retrieval-Augmented Generation.
- Have practical experience deploying models on major cloud providers including Azure AWS or GCP.
- Be familiar with MLOps frameworks such as MLflow and Kubeflow.
Required Skills:
Pandas NumPy Spark SQL Docker Kubernetes Git CI/CD REST APIs & Microservices Pinecone Weaviate FAISS Model monitoring & automated retraining Python TensorFlow PyTorch Scikit-learn Hugging Face LangChain LLMs RAG Azure ML / Azure OpenAI / AWS SageMaker / GCP Vertex AI MLflow Kubeflow MLOps CNN RNN Transformers GANs Diffusion Models
Required Education:
Bachelors Degree (MBA Preferred)
