How You’ll Make an Impact

• Create end-to-end workflows for the training and inference pipeline of the machine learning models.
• Responsible for designing, developing, and implementing multi-step RAG (Retrieval-Augmented Generation), agentic, or tool-augmented workflows using Python and frameworks like LangChain and LangGraph.
• Know the latest advancements in agentic AI, large language model (LLM) orchestration, and tools within the Python ecosystem.
• Build and optimize RAG pipelines using vector stores such as FAISS, AWS OpenSearch.
• Implement solutions for processing and analyzing time series data using libraries like Pandas and NumPy, enhancing data-driven decision-making.
• Write, evaluate, and optimize prompts for LLMs to improve the accuracy and efficiency of AI-driven applications.
• Collaborate with other developers to create, deploy, and maintain applications for different platforms.
• Write and review code for multiple applications, ensuring high quality and readability.
• Conduct unit testing and integration analysis to refine product performance.
• Ensure consistency between delivered product features and business requirements.
• Optimize application performance and resolve issues across different platforms.

What You Bring

• You hold a Bachelor’s degree in Computer Science, Information Technology, Software Engineering, or a related field.
• Proven 3+ years of experience as a Machine Learning Engineer or AI Developer in building complex AI-driven applications.
• Proficient programming experience in Python, with hands-on knowledge of libraries such as scikit-learn, Numpy, Pandas, Langchain, LangGraph, TensorFlow, or PyTorch.
• Familiarity with building APIs (Application Programming Interfaces) and integrating third-party libraries.
• Understanding of AWS services for deploying FastAPI applications (e.g., Lambda, S3, ECS, SageMaker, StepFunctions) or Basic understanding of the Azure services.
• Familiarity with the Agile development lifecycle.
• Knowledge of version control tools such as Git and CI/CD processes using Jenkins or similar tools.
• Strong problem-solving and critical-thinking abilities.
• Strong communication skills to support engagement with various collaborators.
• Ability to work under pressure and adhere to tight deadlines.
• Capability to switch between different projects as needed (e.g., application development vs. AI/ML Research).
• Experience with backend integrations relevant to machine learning applications and data pipelines, such as AWS services (e.g., SageMaker, Lambda, S3, Step Function) or other cloud-based platforms (Azure).
• Understanding of standard methodologies for deploying and managing AI/ML models in production environments.
• Understanding of time series analysis techniques and familiarity with handling time-dependent data using libraries such as Sklearn, Pandas and NumPy, along with knowledge of leveraging LLMs for various natural language processing tasks and user interactions in AI applications.
• Prior experience with model testing and validation frameworks (e.g., MLflow, Pytest for Python) to ensure the robustness and reliability of machine learning solutions.
• Proficiency in data engineering practices, including data wrangling, cleaning, and preprocessing for machine learning tasks.