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.
Key Responsibilities
- 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, SageMaker, and Amazon Q in QuickSight.
- Integrate AI services into web and application layers (e.g., via REST APIs, LangChain, 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, QuickSight, 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).
Required Skills & Experience
- 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., NumPy, Pandas, scikit-learn, LangChain, PyTorch, or TensorFlow).
- Experience with AWS AI/ML ecosystem, including Bedrock, SageMaker, 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 QuickSight (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.
Preferred Qualifications
- Experience deploying chatbots, retrieval-augmented generation (RAG), or embedding-based search.
- Demonstrated experience in applying AI 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.
Tech Stack You’ll Work With
- Languages: Python, SQL, JSON
- Cloud: AWS (Bedrock, SageMaker, Lambda, RDS, S3, Glue)
- Databases: AWS RDS, SQL Server
- Analytics: Power BI, QuickSight with Q
- AI/ML Tools: LangChain, Bedrock SDK, PyTorch, scikit-learn, Hugging Face Transformers
Tags & focus areas
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