AI Engineer

MSCI Services

Throughout your work, you'll apply best practices in software engineering and system architecture. If you're passionate about delivering high-impact AI solutions in a dynamic environment, this is an exciting opportunity to have a substantial influence on our AI capabilities through your expertise.

Responsibilities:

• Design and implement

  scalable distributed systems

  .
• Architect solutions that can handle

  large volumes of data

  for real-time and batch processing.
• Design and develop

  efficient AI pipelines

  with automation and reliability across the platform.
• Integrate

  agentic workflows and AI agents

  into data extraction processes, and enable systems to perform multi-step reasoning and tool usage to improve accuracy and efficiency of data extraction.
• Deploy, monitor, and maintain

  LLM-based extraction systems in production

  , ensuring reliability and scalability. Set up appropriate monitoring, logging, and evaluation metrics to track performance, and perform continual tuning and improvements based on human-in-the-loop feedback.
• Conduct applied research and experimentation with the

  latest generative AI models and techniques

  to enhance extraction capabilities. Prototype new approaches and iterate quickly to integrate successful methods into the production pipeline.
• Collaborate with

  cross-functional teams

  (data engineers, product managers, domain experts) to gather requirements and align AI solutions with business needs.

Qualifications:

• Experience in

  applied AI or machine learning engineering

  , with a track record of building and deploying AI solutions (especially in NLP).
• Hands-on experience with using

  Generative AI models and APIs/frameworks

  (e.g., OpenAI GPT-4, Google Gemini).
• Ability to build

  Agentic AI systems

  where LLMs interact with tools or perform multi-step workflows.
• Proficiency in

  Python

  (preferred) and experience deploying machine learning models or pipelines at scale.
• Good understanding of

  embeddings, LLM models

  , and experience with

  retrieval-augmented generation (RAG) workflows

  to incorporate external knowledge into LLM-based systems.
• Knowledge of

  LLMOps and cloud services

  (Azure, GCP, or similar) for deploying and managing AI solutions.
• Experience with containerization, orchestration, and monitoring of ML models in a production cloud environment.
• Excellent

  collaboration and communication skills

  , with the ability to work effectively in a team, translate complex technical concepts to non-technical stakeholders, and document work clearly.