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

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