Gen Ai Vishwajeet

Key Responsibilities

AI-Led SDLC Orchestration

Design and operationalize AI-assisted workflows across requirements design coding testing DevOps and release management.

Implement multi-agent orchestration patterns for parallel SDLC activities such as planning development testing and validation.

Enable intent-driven development converting business intent into structured backlogs epics and user stories using GenAI agents.

AI Validation & Human-in-the-Loop Governance

Define and enforce AI validation checkpoints for code quality security compliance and architectural conformance.

Act as the AI Validator / Engineering Authority for AI-generated artifacts (code test cases documentation).

Ensure responsible AI usage traceability auditability and explainability across SDLC stages.

Engineering & Platform Enablement

Integrate GenAI capabilities with CI/CD pipelines DevOps toolchains and repositories.

Enable automated test generation AI-assisted defect analysis and self-healing pipelines.

Drive adoption of policy-as-code automated quality gates and telemetry-driven engineering insights

2) Skill - AI Prompt Engineering

B2- 142815

B3- 142816

C1- 142813

GenAI / Agentic AI Developer hand on Skill Sets

1. Core Python Development Experience

Strong proficiency in Python programming and application development.

2. MCP Development Using Python

o Experience developing MCP Client (mandatory) and MCP Server( Optional)

o Ability to implement an MCP Client within an Agentic AI RAG workflow.

3. Agentic AI Workflow Development

o Knowledge of building Agentic AI workflows using the LangGraph / Crew AI Python framework.

4. GenAI Application Development

o Experience building GenAI applications using the Lang Chain Python framework.

o Basic understanding of Vector Databases such as Pinecone Chroma DB and PGVector.

o Hands-on experience implementing a vanilla RAG pipeline using LangChain python framework for text data.

o Fine tuning of LLM Model

5. GenAI Application Development Advanced level exp - Hands-on experience implementing a RAG pipeline for Unstructure data ( Image) - CLIP model - Optional skill

6. Transformer & LLM Fundamentals

o Understanding of Transformer architecture.

o Knowledge of how encoder and decoder mechanisms work in LLMs.

7. Deep Learning & NLP Fundamentals

o Basic knowledge of Deep Learning concepts and algorithms such as ANN CNN and LSTM.

o Understanding of how neural networks working using Gradient Descent algorithms.

3) GenAI Consulting for Apps & Infra

140465 -2

Having Project execution experience with Generative AI and Python