Develop and deploy solutions utilizing Large Language Models (LLMs) and Natural Language Processing (NLP) technologies coded in Python.
Design, implement, and optimize workflows using LangChain, OpenAI APIs
Work with both GraphDB (e.g., Neo4j, ArangoDB) and VectorDB (e.g., Pinecone, Weaviate) technologies for data storage and retrieval in AI applications.
Test different AI models (e.g., OpenAI, Claude) to evaluate their performance and ability to succeed at specific tasks, ensuring the best fit for each application.
Collaborate with team members to integrate AI models into scalable, production-ready applications.
Provide technical leadership in Generative AI projects, leveraging prior hands-on experience.
Develop, deploy, and maintain machine learning models and pipelines using Python.
Design, implement, and optimize traditional ML workflows, ensuring efficiency and scalability.
Collaborate with data scientists and analysts to preprocess and clean datasets for training and evaluation.
Conduct experiments with various machine learning algorithms (e.g., decision trees, SVM, neural networks) to identify the best-performing models for specific tasks.
Integrate ML models into production systems, ensuring robust performance and monitoring.
Requirements:
5+ years of experience in Python development. (5 years is just coding not AI. For the Jr it might be less, but for the Sr, we want this.)
Deep understanding of LLMs and NLP technologies.
Hands-on experience with LangChain and OpenAI platforms.
Experience with GraphDB (e.g., Neo4j, ArangoDB) and VectorDB (e.g., Pinecone, Weaviate) for database management.
Proven experience in Generative AI projects with references or examples.
Strong understanding of traditional machine learning algorithms and their applications.
Hands-on experience with frameworks such as scikit-learn, TensorFlow, or PyTorch.
Proficiency in data manipulation and analysis using libraries like Pandas and NumPy.
Familiarity with data visualization tools (e.g., Matplotlib, Seaborn) for presenting findings.
Experience with data storage solutions, including relational databases (e.g., PostgreSQL, SQL Server) and NoSQL databases (e.g., MongoDB, Redis).
Bonus Skills:
Experience with Databricks
Familiarity with AutoML tools and techniques.
Any Certificate related to AI / ML
Other database technology - Postgres and SqlServer
NoSQL database experience would be more helpful - Mongo, ElasticSearch
JSON experience, and development of API's in coding experience
Experience with cloud platforms (e.g., AWS, Azure, GCP) for deploying ML models
Familiarity with containerization technologies (e.g., Docker, Kubernetes) for model deployment
Understanding of MLOps practices for managing the ML lifecycle.
