AI Engineer

About the Role

We are seeking a hands‑on AI Engineer to help design, build, and deploy intelligent features within our application ecosystem. This role will collaborate closely with our Manager of Data Science & AI Engineering to identify opportunities for generative AI, predictive analytics, and automation across business workflows.

You will be responsible for scoping, prototyping, and implementing AI products — from model design to integration — leveraging both proprietary data and leading cloud AI platforms.

• Partner with the Manager of Data Science & AI Engineering to scope and deliver AI-driven product features and internal tools.
• Design and implement machine learning and generative AI solutions using cloud services such as AWS Bedrock, Sage Maker, and Amazon Q in Quick Sight.
• Integrate AI services into web and application layers (e.g., via REST APIs, Lang Chain, or Bedrock SDK).
• Develop proof‑of‑concepts for natural language querying, document summarization, forecasting, and user experience enhancements using AI.
• Work with structured and unstructured data stored in AWS RDS, SQL Server, and other data sources.
• Collaborate with data engineering and analytics teams using tools like Power BI, Quick Sight, and Python‑based data pipelines.
• Ensure responsible AI design, including model monitoring, bias testing, and performance validation.
• Stay current with emerging technologies in AI (LLMs, vector databases, RAG architectures, and MLOps best practices).

• 3–5 years of experience as an AI Engineer, Data Scientist, or Machine Learning Engineer.
• Practical experience with agentic AI.
• Strong proficiency in Python (e.g., Num Py, Pandas, scikit‑learn, Lang Chain, PyTorch, or Tensor Flow).
• Experience with the AWS AI/ML ecosystem, including Bedrock, Sage Maker, Lambda, and Step Functions.
• Experience with LLM integration and prompt engineering (e.g., OpenAI, Anthropic Claude, Amazon Titan, etc.).
• Experience with SQL and data modeling using AWS RDS or SQL Server.
• Comfort working across analytics and visualization tools such as Power BI or Amazon Quick Sight (with Q).
• Understanding of MLOps concepts such as versioning, CI/CD, and monitoring.
• Familiarity with prompt engineering.
• Experience mapping domain business problems into building deep neural networks for predictive insights.
• Ability to plan and implement a training validation strategy.
• Strong problem‑solving skills, product mindset, and ability to translate ambiguous business requirements into deliverable AI solutions.

• Experience deploying chatbots, retrieval‑augmented generation (RAG), or embedding‑based search.
• Demonstrated experience in applying AI to complex domains with large numbers of entities and relationships.
• Proven track record in building AI applications for end‑users.
• Experience validating the performance of AI applications and incrementally improving accuracy.
• Knowledge of API integration and orchestration frameworks (FastAPI, Flask, or Streamlit).
• Understanding of responsible AI principles and data governance best practices.
• Experience integrating AI with BI or analytics dashboards for end‑user insights.

• Languages:
  
  Python, SQL, JSON
• Cloud: AWS (Bedrock, Sage Maker, Lambda, RDS, S3, Glue)
• Databases: AWS RDS, SQL Server
• Analytics:
  Power BI, Quick Sight with Q
• AI/ML Tools:
  Lang Chain, Bedrock SDK, PyTorch, scikit‑learn, Hugging Face Transformers