Structure business problems and drive viable, data-driven hypotheses in collaboration with business partners
Ability to skillfully enumerate a business problem, quantify its impact, size relevant data, and document applicable sources
Devise, develop and disseminate actionable intelligence from disparate data sources using advanced data analytics tools and techniques
Ability to identify needs and opportunities for advancements in innovations, processes and automation
Able to work proactively and take initiative without being specifically directed
Ability to extract & aggregate data from disparate data sources
Agentic AI Development: Design, build, and deploy agentic AI systems using frameworks such as LangChain, LangGraph, and related libraries.
Develop and deploy multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration.
RAG Pipelines: Implement and optimize RAG systems, ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses.
LLM Engineering: Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation. Work with LLM/SLM APIs, embeddings, and advanced generative AI techniques.
Enterprise AI Platform: Lead the development of enterprise-grade AI platforms integrating LLMs, RAG, embeddings, and agentic AI protocols.
Implement and standardize Model Context Protocol (MCP) for consistent context management across models and agents.
MLOps & Observability: Establish and enforce best practices for MLOps, monitoring, and observability, ensuring scalable and maintainable AI solutions.
Ability to perform in depth data analysis including but not limited to
Machine Learning
Classification
Optimization
Time Series analysis
Pattern Recognition
Establish and develop end-to-end automated processes (i.e.: data analyses, model development & implementation, manual processes, etc)
Ability to communicate complex topics in an easy-to-understand manner when presenting to management
Ability to visualize data and intelligence in easy to understand story telling
Required Skills
5+ years overall experience in software development, data science, or machine learning.
1+ year of hands-on experience developing AI applications with LLMs and systems such as retrieval-based methods, fine-tuning, or agent-based architectures.
Strong programming skills in Python and basics in SQL.
Expertise with LLM/SLM APIs, embeddings, and RAG systems.
Experience deploying on Google Cloud Platform (GCP) with Vertex AI, and IBM WatsonX.
Familiarity with agentic AI protocols and exposure to Agent Development Kits (ADKs).
Experience implementing Model Context Protocol (MCP) for agent coordination.
Prior exposure to LangGraph, AutoGen, or related orchestration frameworks.
1+ year of experience with frameworks like LangChain, LlamaIndex, OpenAI, or similar tools.
1 or more years’ experience in Cyber Security domain knowledge especially on IAM tools like Sailpoit, CyberArk and SIEM/SOAR tools like Splunk etc.
Preferred
2 or more years’ experience with developing Robotic Process Automation and/or automation efforts
2 or more years using Azure OR AWS cloud services
2 or more years skilled at data visualization (Tableau, PowerBI, etc.)
2 or more years with modern data engineering with APIs
2 or more years applying agile SDLC
Experience in enterprise-scale deployments of AI-driven platforms.
Contributions to open-source AI/ML projects are a plus.