Grade - E2

Key Responsibilities

Technical Leadership & Delivery

• Lead end-to-end delivery of sophisticated AI platforms and products, ensuring scalability, reliability, and performance.
• Build and mentor cross-functional teams of developers, data scientists, and ML engineers.
• Define architectural blueprints, enforce engineering best practices, and champion responsible AI principles.
• Stay ahead of emerging AI trends and ensure solutions leverage the latest frameworks and platforms.

Hands-on Development

• Actively design, code, and deploy production-grade AI solutions using Python (and familiarity with Java/Scala).
• Build, fine-tune, and operationalize Large Language Models (LLMs) across OpenAI (GPT models), Anthropic (Claude), Google Gemini, Meta LLaMA, Falcon, and Mistral.
• Apply advanced frameworks and libraries such as LangChain, LlamaIndex, Hugging Face Transformers, and RAG (Retrieval-Augmented Generation) architectures.
• Agentic Frameworks & MCP Integration (critical focus)
• Autonomous & Multi-Agent Systems: Develop AI agents and multi-agent architectures that orchestrate tasks, collaborate, and divide work for complex workflows.
• MCP Server Integration: Build and deploy Model Context Protocol (MCP) servers to expose tools and data, enabling agents to interact with external systems and databases.
• Agentic Framework Expertise, Standardized Interfaces, Multi-Agent Systems, Deep Understanding of Agentic Frameworks

Platforms & Infrastructure

• Deploy AI workloads on enterprise-grade platforms
• Architect AI/ML pipelines with Kubernetes (K8s), Docker, Terraform, and GitHub Actions for CI/CD.
• Integrate vector databases (Pinecone, Weaviate, Milvus, ChromaDB, Redis Vector Search) and knowledge graphs (Neo4j) into scalable production systems.
• Ensure data pipelines leverage Delta Lake, BigQuery, Elasticsearch, and other modern data architectures.

DevOps, CI/CD & Testing

• Implement modern DevOps practices, ensuring automation, scalability, and high availability of AI solutions.
• Design and maintain CI/CD pipelines for rapid deployment of AI models and services.
• Apply LLM testing, evaluation, and monitoring techniques (including Deepeval, unit/integration testing for AI agents, and benchmark validation).
• Monitor model drift, performance, and safety using best-in-class tools.

Presales & Client Engagement

• Act as a trusted advisor during pre-sales engagements, helping shape AI strategies and solutions tailored to client needs.
• Author and present winning proposals, RFP responses, and solution blueprints that differentiate our offerings.
• Collaborate closely with sales teams to drive business growth, influencing C-level and senior decision-makers.
• Deliver thought leadership through workshops, demos, and speaking engagements at industry forums.

Executive Presence & Leadership

• Represent the company in senior executive discussions, client boards, and industry panels.
• Translate highly complex AI concepts into business-impact storytelling that resonates with non-technical leaders.
  • Inspire confidence, build long-term relationships, and establish the organization as a leader in enterprise AI