Duties & Responsibilities
Translate business requirements into scalable and well -documented ML pipelines and AI solutions using Databricks, Azure AI, and Snowflake.
Architect and implement scalable GenAI solutions using Azure/ GCP AI, Databricks, and Snowflake
Develop and deploy agentic workflows using LangChain, LangGraph, and OpenAI Agents SDK for autonomous task execution.
Lead experimentation and fine -tuning of LLMs (e.g., GPT -4, Claude, LLaMA 2) for enterprise use cases such as summarization, personalization, and content generation.
Integrate GenAI models into business applications with Human -in -the -Loop (HITL) validation and feedback loops.
Build and maintain MLOps/LLMOps pipelines using MLflow, ONNX, and Unity Catalog for reproducibility and governance.
Monitor model performance and ensure responsible AI operations through observability tools like OpenTelemetry and Databricks AI Gateway. Stay current with GenAI and LLM advancements, including frameworks like LangChain, LlamaIndex, and Gemini, and apply them to enterprise use cases.
Requirements
Basic Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
5–8 years of experience in AI/ML engineering, with at least 2 years focused on GenAI and LLMs.
Proven experience deploying agentic AI systems in production environments.
Strong understanding of NLP, deep learning, and multi -modal AI (text, image, audio).
Experience with enterprise -grade AI governance and security practices.
Preferred Qualifications
Languages: Python, SQL, PySpark
Agent frameworks: LangChain, LangGraph, Hugging Face, OpenAI SDK, Gemini
GenAI Tools: Azure AI Foundry, Vertex AI, Databricks AI
MLOps/LLMOps: MLflow, ONNX, Unity Catalog
Data Platforms: Databricks, Snowflake, Data Lake, Knowledge graphs
Understanding of AI governance, including model explainability, fairness, and security (e.g., prompt injection, data leakage mitigation).
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