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AES - DE - Generative AI Prompt Engineers at ZENSAR TECHNOLOGIES SINGAPORE PTE LTD

Pune, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

13 Aug, 26

Salary

0.0

Posted On

15 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Prompt Engineering, LLM APIs, RAG Pipelines, LangChain, LlamaIndex, Kubernetes, Docker, Pytest, Vector Stores, LLM Evaluation, Pydantic, GitOps, Fine-tuning, OpenTelemetry, Guardrails AI

Industry

IT Services and IT Consulting

Description

Key Skills Required * Core Engineering * Strong Python; solid OOP, typing, packaging * Test automation frameworks: pytest, Playwright/Cypress, Pact (contract testing) * Git, GitHub Actions / FluxCD, GitOps workflows * Kubernetes, Docker, Helm basics * AI / LLM Engineering * Hands-on with LLM APIs (OpenAI, Anthropic, Azure OpenAI) and prompt engineering * RAG pipelines, embeddings, vector stores (FAISS, pgvector, or similar) * LangChain / LlamaIndex or equivalent orchestration frameworks * Fine-tuning and model adaptation basics (LoRA / PEFT awareness) * Evaluation & Observability * LLM evaluation frameworks: Ragas, DeepEval, promptfoo, LangSmith * Metrics: groundedness, faithfulness, accuracy, latency, token cost * Golden dataset design and regression harness setup * Observability: OpenTelemetry, LangFuse, MLflow * Guardrails & Trust * NeMo Guardrails, Guardrails AI, Llama Guard, or custom policy engines * Output schema validation (Pydantic, JSON schema) * PII / IP leakage detection, hallucination checks, rate-limit and cost controls * Quality & DevOps * SonarQube, static analysis, code review automation * CI/CD integration of AI tests and evals * Familiarity with Speckit-driven and agentic delivery workflows * Soft Skills * Collaborative mindset — works across GenAI Architect, domain developers, and QA * Clear documentation and evidence-based reporting * Comfortable operating in a hybrid Zensar + Vanderlande POD environment

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Responsibilities

The role involves deciding between AI and deterministic engineering to build efficient, scalable solutions. It focuses on designing RAG pipelines, implementing guardrails, and establishing evaluation frameworks to ensure AI reliability and safety.