Agentic AI Lead

Job Summary

Agentic

AI

Lead

Key Responsibilities

• Architect Agentic AI Systems: Design and implement agentic AI frameworks that enable autonomous, goal-oriented systems capable of reasoning, planning, and decision-making.
• Develop Generative AI Solutions: Build and deploy generative AI models (e.g., LLMs, GANs, VAEs) for applications such as content generation, data augmentation, and creative automation.
• Leverage Databricks: Utilize Databricks for end-to-end AI workflows, including data ingestion, preprocessing, model training, deployment, and monitoring.
• Optimize AI Pipelines: Create scalable, efficient, and robust AI pipelines on Databricks, integrating with tools like Delta Lake, MLflow, and Spark for data processing and model management.
• Collaborate with Teams: Work closely with data scientists, engineers, and business stakeholders to translate requirements into technical solutions.
• Drive Innovation: Stay updated on advancements in agentic AI, generative AI, and Databricks capabilities, proposing innovative solutions to enhance system performance and capabilities.
• Ensure Scalability and Reliability: Architect solutions that are scalable, secure, and optimized for performance in production environments.
• Mentor and Guide: Provide technical leadership and mentorship to junior team members on AI best practices and Databricks workflows.
  

Required Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
• Experience:
• 5+ years of experience in AI/ML architecture, with at least 2 years focused on generative AI and/or agentic systems.
• 3+ years of hands-on experience with Databricks, including Delta Lake and Spark.
• Proven track record of designing and deploying production-grade AI solutions.
• Technical Skills:
• Expertise in generative AI models (e.g., GPT, BERT, Stable Diffusion) and agentic AI frameworks (e.g., LangChain, AutoGen, llamaindex, langraph).
• Proficiency in Databricks for data engineering, model training, and deployment.
• Strong programming skills in Python, PySpark, and SQL.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and their integration with Databricks.
• Soft Skills:
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities to work with cross-functional teams.
• Ability to lead projects and mentor team members.
  

Preferred Qualifications

• Experience with reinforcement learning, multi-agent systems, or autonomous AI agents.
• Knowledge of Databricks-specific tools like Databricks SQL, Feature Store, or AutoML.
• Familiarity with additional AI frameworks (e.g., TensorFlow, PyTorch, Hugging Face).
• Certifications in Databricks, AWS, Azure, or Google Cloud.