Assistant Vice President

Responsibilities AI Architecture & Solution Design Architect enterprise-grade GenAI solutions using LLMs, embeddings, and vector databases. Design scalable RAG pipelines and knowledge-grounded AI systems. Define agentic workflows with reasoning, tool usage, and memory capabilities. Establish secure, compliant AI deployment architectures across cloud platforms. Agentic AI & Automation Design multi-agent systems for workflow automation and decision intelligence. Implement orchestration logic, tool integration layers, and human-in-the-loop controls. Define evaluation, guardrails, and monitoring frameworks for agent performance. AI Platform Management & Operational Excellence Establish standards and best practices for LLMOps / MLOps, covering the full model lifecycle from development to production. Assess and select foundation models (OpenAI, open-source LLMs) for suitability, performance, and compliance in enterprise contexts. Ensure AI solution efficiency and robustness by optimizing cost, latency, scalability, and system reliability. Client Advisory & Pre-Sales Support Act as AI solution architect in client discussions and transformation initiatives. Lead PoCs, technical demonstrations, and innovation workshops. Translate business objectives into scalable AI system designs. Innovation & Enablement Stay current with evolving GenAI and agent frameworks. Develop architectural playbooks, reference patterns, and reusable accelerators. Mentor engineering teams on best practices in AI system design. Experience and Competency Requirements 8-12 years of experience in AI/ML engineering and architecture. Minimum 2-3 years hands-on experience with Generative AI systems. Strong expertise in LLMs, RAG architectures, embeddings, and vector stores. Experience designing and deploying production-grade AI applications. Hands-on experience with cloud-native AI deployments (AWS / Azure / GCP). Strong problem-solving and client-facing communication skills. Ability to operate in a consulting or managed services environment. Should have decent to good experience in data handling and analytics with python Nice to have capabilities Previous experience in pre-sales & consulting is preferred. Experience leading enterprise AI transformation initiatives. Exposure to industry-specific AI applications (Insurance, Healthcare, Banking, Media). Experience integrating AI into large-scale operational workflows. Skills GenAI & LLM Frameworks (Mandatory) OpenAI APIs / Azure OpenAI LangChain / LangGraph / LlamaIndex Transformers (Hugging Face) Prompt engineering and evaluation frameworks Agentic Systems & Orchestration Multi-agent design patterns (MCP, A2A, ReAct etc) Tool integrations and API orchestration Memory frameworks and contextual reasoning Guardrails, observability, and monitoring Data & Infrastructure Vector databases (Pinecone, FAISS, Weaviate or equivalent) Python, FastAPI, REST services Docker, Kubernetes Cloud platforms (AWS, Azure, GCP) Data Handling & Analytics Skills Data preprocessing and ETL for structured and unstructured data Data manipulation using Pandas, NumPy, and SQL Exploratory data analysis (EDA) and statistical analysis Data visualization (Matplotlib, Seaborn, Plotly, Tableau, Power BI) Metrics design for AI evaluation, monitoring, and performance measurement Knowledge of data quality, validation, and governance best practices Advanced Capabilities Fine-tuning and model evaluation AI governance and responsible AI Cost optimization and performance benchmarking Responsibilities AI Architecture & Solution Design Architect enterprise-grade GenAI solutions using LLMs, embeddings, and vector databases. Design scalable RAG pipelines and knowledge-grounded AI systems. Define agentic workflows with reasoning, tool usage, and memory capabilities. Establish secure, compliant AI deployment architectures across cloud platforms. Agentic AI & Automation Design multi-agent systems for workflow automation and decision intelligence. Implement orchestration logic, tool integration layers, and human-in-the-loop controls. Define evaluation, guardrails, and monitoring frameworks for agent performance. AI Platform Management & Operational Excellence Establish standards and best practices for LLMOps / MLOps, covering the full model lifecycle from development to production. Assess and select foundation models (OpenAI, open-source LLMs) for suitability, performance, and compliance in enterprise contexts. Ensure AI solution efficiency and robustness by optimizing cost, latency, scalability, and system reliability. Client Advisory & Pre-Sales Support Act as AI solution architect in client discussions and transformation initiatives. Lead PoCs, technical demonstrations, and innovation workshops. Translate business objectives into scalable AI system designs. Innovation & Enablement Stay current with evolving GenAI and agent frameworks. Develop architectural playbooks, reference patterns, and reusable accelerators. Mentor engineering teams on best practices in AI system design. Experience and Competency Requirements 8-12 years of experience in AI/ML engineering and architecture. Minimum 2-3 years hands-on experience with Generative AI systems. Strong expertise in LLMs, RAG architectures, embeddings, and vector stores. Experience designing and deploying production-grade AI applications. Hands-on experience with cloud-native AI deployments (AWS / Azure / GCP). Strong problem-solving and client-facing communication skills. Ability to operate in a consulting or managed services environment. Should have decent to good experience in data handling and analytics with python Nice to have capabilities Previous experience in pre-sales & consulting is preferred. Experience leading enterprise AI transformation initiatives. Exposure to industry-specific AI applications (Insurance, Healthcare, Banking, Media). Experience integrating AI into large-scale operational workflows. Skills GenAI & LLM Frameworks (Mandatory) OpenAI APIs / Azure OpenAI LangChain / LangGraph / LlamaIndex Transformers (Hugging Face) Prompt engineering and evaluation frameworks Agentic Systems & Orchestration Multi-agent design patterns (MCP, A2A, ReAct etc) Tool integrations and API orchestration Memory frameworks and contextual reasoning Guardrails, observability, and monitoring Data & Infrastructure Vector databases (Pinecone, FAISS, Weaviate or equivalent) Python, FastAPI, REST services Docker, Kubernetes Cloud platforms (AWS, Azure, GCP) Data Handling & Analytics Skills Data preprocessing and ETL for structured and unstructured data Data manipulation using Pandas, NumPy, and SQL Exploratory data analysis (EDA) and statistical analysis Data visualization (Matplotlib, Seaborn, Plotly, Tableau, Power BI) Metrics design for AI evaluation, monitoring, and performance measurement Knowledge of data quality, validation, and governance best practices Advanced Capabilities Fine-tuning and model evaluation AI governance and responsible AI Cost optimization and performance benchmarking Bachelor’s degree required M.Tech/ MS in Computer Science, AI, or related field preferred; Required Experience: 8-12 years