Senior AI Forward Deployed Engineer - VP Level Role

Responsibilities

• Develop scalable AI Agentic frameworks, ML pipelines, and cloud-native applications.
• Design end-to-end solutions and mentor teams.
• Lead complex implementations and advise clients in Insurance, Healthcare, and Banking sectors.
• Facilitate high-impact client workshops and strategy sessions.
• Lead large-scale, complex Generative AI and Agentic AI projects.
• Mentor and guide technical teams in advanced AI development.
• Build and maintain strong relationships with key stakeholders.
• Ensure adherence to rigorous software engineering principles and responsible AI practices.
• Stay current with the latest trends and technologies in Generative AI, Large Language Models, and Agentic AI.

Qualifications

• Technical Skills: Proficiency in Python, Langchain, LangGraph, RAG systems, AI Agents, Agentic Frameworks, ScikitLearn, Numpy, Pandas, Gradient Boost models, Ensembles, Reinforcement Learning, LSTMs, Transformers, RlLib, AWS SageMaker, AWS BedRock, NLP, ML pipelines, Docker, Kubernetes, Terraform, FastAPI, PostgreSQL, ReactJS, Redis, GCP, CI/CD (Jenkins, GitHub Actions), Prompt engineering, cross-functional collaboration, mentorship, 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).
• 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., LangChain, LlamaIndex, Langfuse) and practical experience with Agentic AI frameworks and concepts (e.g., AutoGen, CrewAI, LangGraph, 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.
• Cloud Platforms: Strong working knowledge and practical deployment experience on at least one major cloud platform (AWS, Azure, GCP), including their AI/ML services.
• LLMOps/MLOps: Expertise in designing and implementing robust MLOps/LLMOps pipelines for automated testing, CI/CD, monitoring, and governance of complex AI models and applications.
• Leverage excellent communication skills to explain complex AI concepts to various stakeholders.
• Project and program management skills with experience in Agile environments.