AI/ML Developer (CE58SF RM 3260)

Position: AI/ML Developer (CE58SF RM 3260)

Candidate Roles and Responsibilities

• Develop and implement machine learning models and algorithms.
• Work closely with project stakeholders to understand requirements and translate them into deliverables.
• Utilize statistical and machine learning techniques to analyze and interpret complex data sets.
• Stay updated with the latest advancements in AI/ML technologies and methodologies.
• Collaborate with cross-functional teams to support various AI/ML initiatives.

Good to have Skills

• Knowledge and Experience in building knowledge graphs in production.
• Understanding of multi-agent systems and their applications in complex problem-solving scenarios.

Technical Skills required:

• Solid Experience in Time Series Analysis, Anomaly Detection and traditional machine learning techniques such as Regression, Classification, Predictive modeling, Clustering, Deep Learning stack using python
• Experience with cloud infrastructure for AI/ ML on AWS(Sagemaker, Quicksight,Athena, Glue).
• Expertise in building enterprise grade, secure data ingestion pipelines for unstructured data(ETL/ELT) – including indexing, search, and advance retrieval patterns.
• Proficiency in Python, TypeScript, NodeJS, ReactJS (and equivalent) and frameworks. (e.g., pandas, NumPy, scikit-learn, SKLearn, OpenCV, SciPy), Glue crawler, ETL
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Quicksight).
• Knowledge of deep learning frameworks (e.g., TensorFlow, Keras, PyTorch).
• Experience with version control systems (e.g., Git, CodeCommit).
• Strong knowledge and experience in Generative AI/ LLM based development.
• Strong experience working with key LLM models APIs (e.g. AWS Bedrock, Azure Open AI/ OpenAI) and LLM Frameworks (e.g. LangChain, LlamaIndex).
• Knowledge of effective text chunking techniques for optimal processing and indexing of large documents or datasets.
• Proficiency in generating and working with text embeddings with understanding of embedding spaces and their applications in semantic search and information. retrieval.
• Experience with RAG concepts and fundamentals (VectorDBs, AWS OpenSearch, semantic search, etc.), Expertise in implementing RAG systems that combine knowledge bases with Generative AI models.
• Knowledge of training and fine-tuning Foundation Models (Athropic, Claud , Mistral, etc.), including multimodal inputs and outputs.

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