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

- Skill - AI Prompt Engineering

B2- 142815

B3- 142816

C1- 142813

GenAI / Agentic AI Developer hand on Skill Sets

- Core Python Development Experience

Solid proficiency in Python programming and application development.





- 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.

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

- GenAI Application Development Advanced level exp - Hands-on experience implementing a RAG pipeline for Unstructure data ( Image) - CLIP model - Optional skill
- Transformer & LLM Fundamentals o Understanding of Transformer architecture.

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

- 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.

- GenAI Consulting for Apps & Infra

140465 -2

Having Project execution experience with Generative AI and Python

📌 Gen Ai Vishwajeet (Bengaluru)
🏢 TalentOla
📍 Bengaluru