Assistant Vice President

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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