•AI/ML Solution Development: Design, develop, and implement scalable and robust AI/ML models and solutions to address Infrastructure monitoring, issues, utilization.
•Agentic AI Systems: Lead the research, design, and development of autonomous AI agents that can perform multi-step, multi-layer tasks with minimal human intervention
•SDLC Integration: Seamlessly integrate AI/ML models and agentic AI systems into existing SDLC tools and workflows (e.g., CI/CD pipelines, version control systems, project management platforms) to drive tangible productivity gains.
•Data Pipeline & MLOps: Establish and maintain robust data pipelines for collecting, processing, and preparing data for AI/ML model training and evaluation. Implement MLOps best practices for continuous integration, deployment, monitoring, and retraining of AI/ML models in production environments.
•Productivity Improvement: Identify opportunities for AI/ML-driven automation and optimization. Measure and report on the impact of implemented AI/ML solutions on key productivity metrics.
•Collaboration & Mentorship: Work closely with cross-functional teams including software engineers, data scientists, product managers, and DevOps specialists to understand their needs, gather requirements, and facilitate the adoption of AI/ML solutions.
Required Qualifications:
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• 5+ years of experience in AI/ML engineering, with a proven track record of deploying models into production.
Required Skills:
• Strong proficiency in Python and relevant AI/ML libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn, Hugging Face).
• Demonstrable experience with Large Language Models (LLMs) and their application in various contexts, including prompt engineering and fine-tuning.
• Solid understanding and practical experience in building and deploying AI agent systems or similar autonomous systems. Familiarity with agentic frameworks (e.g., LangChain, LangSmith, LangServe, Chainlit, LangGraph, Llama Index, AutoGPT, Semantic Kernel) is highly desirable.
• Experience with techniques for grounding generative models with external knowledge sources like Retrieval-Augmented Generation (RAG) enhances Named Entity Recognition (NER) for building custom models, React Agentic Framework, Streamlit/Gradio for building quick prototypes, information retrieval from custom documents: RAG-based + newer approaches like CRAG.
• Prompt writing & engineering: Zero Shot, Few Shot, Tree/Chain of Thought prompting principle with hands-on experience.
• Proficiency in data manipulation, cleaning, and feature engineering and experience in Graph Database like neo4J
• Experience with cloud platform AWS and their AI/ML services.
• Strong problem-solving, analytical, and critical thinking skills.
• Excellent communication and collaboration skills, with the ability to articulate complex technical concepts to non-technical stakeholders.
• Experience working with Git, JIRA, Confluence,
• Prompt writing & engineering: Zero Shot, Few Shot, Tree/Chain of Thought prompting principle with hands-on experience.
We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.