AI Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Delivering solutions for the deployment, execution, validation, monitoring, and enhancement of MLE solutions.

• Developing scalable machine learning systems.

• Constructing reusable production data pipelines for machine learning models that have been implemented.

• Producing production-quality code and libraries that can be packaged as containers for installation and deployment.

• Working collaboratively with cross-functional teams and business partners to shape current and future strategies by utilizing analytical skills.

⛳️ Requirements

• Programming Languages: Strong proficiency in Python is required.

• Agentic AI: In-depth expertise in LangChain/LangGraph, CrewAI, Semantic Kernel/Autogen, and Open AI Agentic SDK.

• Machine Learning Frameworks: Familiarity with TensorFlow, PyTorch, Scikit-learn, and AutoML is essential.

• Generative AI: Practical experience with generative AI models, RAG (Retrieval-Augmented Generation) architecture, and Natural Language Processing (NLP).

• Cloud Platforms: Knowledge of AWS (SageMaker, EC2, S3) and/or Google Cloud Platform (GCP) is preferred.

• Data Engineering: Strong skills in data preprocessing and feature engineering.

• Version Control: Experience with GitHub for version control is necessary.

• Development Tools: Proficiency in development tools such as VS Code and Jupyter Notebook.

• Containerization: Experience with Docker for containerization and deployment techniques.

• Data Warehousing: Familiarity with Snowflake and Oracle is an added advantage.

• APIs: Understanding of AWS Bedrock API and/or other GenAI APIs.

• Data Science Practices: Skills in model development, testing/validation, and deployment.

• Collaboration: Experience working within an Agile framework is beneficial.

• RAG Architecture: Knowledge of data ingestion, retrieval, and generation using optimal methods such as hybrid search.

• Insurance/Financial Domain: Familiarity with the insurance industry is highly desirable.

• Google Cloud Platform: Working knowledge is advantageous.

• Industry Experience: 8+ years of experience in AI/ML and data engineering, with a proven track record in large-scale programs and addressing complex use cases using GCP AI Platform/Vertex AI.

• Agentic AI Architecture: Strong command in Agentic AI architecture, development, testing, and research of both Neural-based & Symbolic agents, leveraging current and next-generation deployments and research patterns.

• Agentic Systems: Expertise in developing agentic systems using techniques such as Multi-agent systems, Reinforcement learning, flexible/dynamic workflows, caching/memory management, and concurrent orchestration. Proficiency with one or more Agentic AI frameworks like LangGraph, Crew AI, Semantic Kernel, etc.

• Python Proficiency: Advanced skills in Python for building large, scalable applications, as well as conducting performance analysis and tuning.

• Prompt Engineering: Strong capabilities in prompt engineering, including the design, development, and refinement of prompts (zero-shot, few-shot, and chain-of-thought techniques) to enhance accuracy and utilize optimization tools.

• IR/RAG Systems: Experience in designing, building, and implementing IR/RAG systems employing Vector DB and Knowledge Graph.

• Model Evaluation: Proficient in evaluating models and their tools, with experience in rigorous A/B testing and performance benchmarking of prompt/LLM variations, leveraging both quantitative metrics and qualitative feedback.

🏝️ Benefits

• This role provides an exceptional opportunity for substantial career advancement in a rapidly growing and challenging entrepreneurial environment, coupled with a high level of individual responsibility.