Senior AI Forward Deployed Engineer - VP

Vice President L1, Senior AI Forward Deployed Engineer

Innovative Senior Software ML Engineer with over 8 years of experience in developing scalable AI Agentic frameworks, ML pipelines, and cloud-native applications. Expertise in Python, Langchain/Lang Graph, and RAG systems. Proven ability to design end-to-end solutions, mentor teams, and drive cross-functional collaboration to achieve impactful results

• Software Engineering: Innovative Senior Software Engineer experience in developing scalable AI Agentic frameworks, ML modeling pipelines, and cloud-native applications. Expertise in Python, Langchain/Lang Graph, and RAG systems. Proven ability to design end-to-end solutions, mentor teams, and drive cross-functional collaboration to achieve impactful results
• AI Architecture Innovation Research & Design: Focus on new innovative methods of designing and architecting AI and GenAI systems and be able to grasp and adapt monthly and weekly AI innovation coming out in the industry and apply it to our clients' use cases and needs in new modern ways.
• Client Advisory & Solutioning: Engage directly with senior client stakeholders (including C-suite) to understand complex business challenges, identify opportunities for GenAI and Agentic AI, and define project scope.
• Domain background: Any domain background in Insurance, Trading, Banking, Credit Risk, Healthcare, and Finance.
• Workshop Facilitation: Design, lead, and facilitate high-impact client workshops and strategy sessions focused on identifying and prioritizing Generative and Agentic AI use cases and roadmap development.
• Technical Leadership & Architecture: Design, architect, and oversee the development and deployment of scalable, robust, and cutting-edge Generative AI and sophisticated Agentic AI systems (including multi-agent workflows) for client and internal projects.
• Project & Engagement Leadership: Lead large-scale, complex Generative AI and Agentic AI projects from strategic conception through successful deployment, managing cross-functional teams (internal and client-side) and ensuring timely delivery of high-quality solutions.
• Technical Mentorship: Mentor and guide technical teams (data scientists, data and AI engineers) in best practices for advanced AI development, deployment, MLOps/LLMOps, and agentic system design.
• Stakeholder Management: Build and maintain strong relationships with key internal and external stakeholders, effectively communicating complex technical concepts and project progress.
• Quality & Best Practices: Ensure adherence to rigorous software engineering principles, Agile methodologies, and responsible AI practices throughout the solution lifecycle.
• Stay Current: Maintain deep expertise in the latest trends, research, tools, and technologies within Generative AI, Large Language Models (LLMs), and Agentic AI paradigms.

Technical Skills:

• Combined skills: Python, Langchain, Lang Graph, RAG systems, AI Agents, Agentic Frameworks, Scikit Learn, Numpy, Pandas, Gradient Boost models, Ensembles, Reinforcement Learning, LSTMs, Transformers, RlLib, AWS Sage Maker, AWS Bed Rock, NLP, ML pipelines, Docker, Kubernetes, Terraform, FastAPI, PostgreSQL, ReactJS, Redis, GCP, CI/CD (Jenkins, Git Hub Actions), Prompt engineering, cross-functional collaboration, iterative development, analytics automation, scalable AI framework
• Programming & Libraries:
  Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Experience using Cursor, Windsurf, Replit, and Github Copilot.
• LLM Expertise:
  Proven experience developing applications leveraging state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude) including prompt engineering, fine-tuning, and evaluation.
• GenAI & Agentic Frameworks:
  Hands-on mastery of core GenAI frameworks (e.g., Lang Chain, Llama Index, Langfuse) and practical experience with Agentic AI frameworks and concepts (e.g., Auto Gen, CrewAI, Lang Graph, agent planning, tool use integration, multi-agent collaboration).
• AI Architecture:
  Deep understanding of AI/ML system architecture patterns, including microservices, event-driven architectures, and patterns specific to RAG (Retrieval-Augmented Generation), Graph RAG, Agentic RAG, and multi-agent systems.
• Data: Knowledge of industry approaches to data engines and data labeling like Scale.ai and Mercor. Experience with auto data-labeling and synthetic data generation techniques.
• Vector Databases & Embeddings:
  Expertise in working with various embedding models and vector databases (e.g., Pinecone, Weaviate, Chroma, FAISS).
• Advanced AI Concepts:
  Strong grasp of advanced techniques such as complex task decomposition for agents, reasoning engines, knowledge graphs, autonomous agent design, and evaluation methodologies for complex AI systems.
• Software Engineering:
  Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems.
• Cloud Platforms:
  Strong…