AI/ML Engineer (CPT) (26485)

SUMMARY:
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POSITION INFO:

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.