Recruitment Room Team
AI/ML Engineer (CPT)
Job Description
ENVIRONMENT:
AN innovative Independent Asset Management Firm seeks to fill the critical role of an AI/ML Engineer who will design, develop, and deploy end-to-end AI/ML solutions and data pipelines that turn raw data into actionable insights, ensuring performance, scalability, and security. Working closely with cross-functional teams, you will help develop and deploy AI solutions, models and systems. Your role will involve researching, prototyping, and integrating cutting-edge AI technologies to solve complex business problems. Applicants will need 3+ years in AI/ML Engineering or Data Engineering roles, with end-to-end delivery with proficiency in Python, familiarity with Java or R. You will also be required to have a solid understanding of Machine Learning frameworks such as LangChain, PyTorch, Tensorflow or Hugging Face.
DUTIES:
Transform requirements and build into AI solutions –
- Collaborate with Data Scientists, Software Engineers, and business stakeholders to define use cases and architecture for ML systems.
- Design and develop integrations with AI/ML technologies and data sources to leverage solutions for use cases.
- Work with AI/ML partners and internal teams to optimize and fine-tune AI models and use cases for performance, scalability, and accuracy.
- Deploy AI models and use cases into production environments, ensuring stability, reliability, and security.
- Monitor and evaluate the performance of deployed models and use cases, ensuring iterative improvements.
- Collaborate with Software Engineers to integrate AI/ML capabilities into existing or new software systems.
- Stay updated with the latest advancements in AI and Machine Learning research and apply relevant techniques to solve business challenges.
- Document and communicate AI solutions, methodologies, and best practices to technical and non-technical stakeholders.
Build scalable data pipelines –
- Design, implement, and maintain data ingestion, preprocessing, and feature-engineering workflows using Big Data frameworks (Spark, Hadoop) and SQL expertia.ai.
Develop and optimize models –
- Research, prototype, and productionize ML algorithms with Python and frameworks such as PyTorch, TensorFlow, LangChain, or Hugging Face.
- Implement AI solutions using programming languages (such as Python) and Machine Learning frameworks (such as LangChain, PyTorch, Tensorflow or Hugging Face).
- Support Data Scientists and other stakeholders with collecting, processing, and analysing datasets to train and validate AI models.
Deploy and monitor in the cloud –
- Containerize and orchestrate workloads (Docker, Kubernetes) and deploy models on Azure ensuring reliability and cost efficiency.
Ensure governance & security –
- Implement data quality checks, versioning, and security best practices throughout ML pipelines in line with InfoSec standards.
REQUIREMENTS:
- 3+ Years in AI/ML Engineering or Data Engineering roles, with end-to-end delivery.
- Proficiency in Python; familiarity with Java or R.
- Understanding of Machine Learning frameworks such as LangChain, PyTorch, Tensorflow or Hugging Face.
- Experience with data preprocessing, feature engineering, and model evaluation techniques.
- Familiarity with Big Data processing frameworks (such as Hadoop or Spark) and SQL.
- Experience with Databricks and Mosaic.
- Hands-on with cloud ML services (e.g., SageMaker, Azure ML) and CI/CD pipelines for model deployment.
Advantageous –
- Knowledge of cloud platforms (such as AWS, Azure, or Google Cloud) and experience with deploying AI models on cloud infrastructure.
ATTRIBUTES:
- Ability to adapt to a fast-paced, dynamic work environment and quickly learn new technologies and techniques.
- Curiosity to learn about Artificial Intelligence, Machine Learning, Deep Learning concepts and data insights engines.
- Strong problem-solving and analytical skills, with the ability to understand complex business requirements and translate them into AI solutions.
- Able to explain complex technical concepts to both technical and non-technical audiences.
