GenAI Engineer
Persistent Systems is seeking a Senior AI/GenAI Engineer in Mumbai to lead the development of production-grade AI systems. The role focuses on architecting scalable LLM and RAG solutions using Azure AI Foundry, managing ML Ops pipelines, and automating end-to-end AI workflows. Candidates must possess deep expertise in Python, Generative AI frameworks, and vector databases within cloud-native environments. This role provides an opportunity to drive technical strategy and mentor junior engineers in a hybrid, growth-oriented work environment.
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Experience
6-10 years
Function
Engineering
Work mode
Hybrid, India
Company
Tier 2
What you will work on
Persistent Systems is seeking a Senior AI/GenAI Engineer in Mumbai to lead the development of production-grade AI systems. The role focuses on architecting scalable LLM and RAG solutions using Azure AI Foundry, managing ML Ops pipelines, and automating end-to-end AI workflows. Candidates must possess deep expertise in Python, Generative AI frameworks, and vector databases within cloud-native environments. This role provides an opportunity to drive technical strategy and mentor junior engineers in a hybrid, growth-oriented work environment.
TAL's take
Solid tier-2 role at an established company with clear AI/GenAI scope and defined senior-level expectations.
The JD is highly specific regarding the stack (Azure AI Foundry, specific RAG frameworks) and provides a clear breakdown of responsibilities.
Salaries at Persistent Systems
16.9 LPA average
Based on 906 Grapevine salary entries for Persistent Systems.
Engineering
0 - 2 years | 3.1
5 LPA average
Range: 4 - 8 LPA
Engineering
2 - 4 years | 3.1
7 LPA average
Range: 4 - 24 LPA
Engineering
4 - 6 years | 3.3
13 LPA average
Range: 7 - 25 LPA
Engineering
6 - 8 years | L5
16 LPA average
Range: 8 - 27 LPA
Must haves
- Strong expertise in Python and AI/ML system design
- Hands-on experience with Generative AI, LLMs, and RAG architectures
- Deep understanding of vector databases
- Experience with Azure AI Foundry
- Proficiency in ML Ops tools and frameworks
Tools and skills
About the company
Established IT services company with strong engineering presence and established product development capabilities.
Posts mentioning Persistent Systems
I have two offers Of same CTC Capgemini and PERSISTENT SYSTEMS which one is good to join?
How much persistent give for 11 years exp salary and grade for a full stack developer and having deveops and cloudops knowledge
Title: Data Scientist (~2 YOE) – Built planning tools, pipelines, and AI system. Need honest feedback on profile.
Hi all, Looking for practical feedback on my profile before I start applying. I’ll keep this structured so it’s easier to evaluate. --- 1) Planning Tools / Web Applications Problem: Forecasting workflows were fragmented and heavily Excel-driven: - Multiple data sources (orders, shipments, different forecast versions) - Manual merging, lookups, and adjustments - No way to simulate scenarios or compare forecasts cleanly - Different planners using different methods → inconsistency What I built: - Two internal applications for planning workflows: - A planning tool integrating 8+ data sources - A forecasting simulator supporting multi-level editing (high → granular) Key capabilities: - Real-time scenario simulation - Side-by-side comparison of multiple forecast types - Hierarchical adjustments across levels - SQL write-back for persistence Scale: - Processes ~150K+ records per cycle - Used in monthly planning cycles by multiple teams Impact: - Removed fragmented Excel workflows - Enabled consistent decision-making across users - Reduced manual effort and improved visibility into forecast behavior --- 2) Automation & Data Pipelines Problem: Core workflows were manual and repetitive: - Multi-file Excel processing - Data cleaning + merging across systems - Version tracking errors - High effort per cycle (1–4 hours depending on workflow) What I built: - Multiple pipelines automating end-to-end workflows Examples: - Large-scale consolidation pipeline: - Input: ~1M+ rows across 20+ files - Output: clean, unified dataset (~75% reduction) - 2nd pipeline: - Replaced a 23-step manual process - Standardized inconsistent formats across datasets - 3rd automation processing: - Automated unpivoting, enrichment, and version tracking Impact: - Reduced processing time from hours → minutes per cycle - Eliminated manual errors (copy-paste, lookup mistakes) - Standardized workflows across users --- 3) Power BI / Monitoring Problem: Recent data (orders/shipments) showed inconsistencies, but: - No visibility into changes over time - Hard to identify where data drift was happening What I built: - Power BI dashboards with: - Hierarchical filters - Drill-down views - Month-over-month comparison Scale: - ~30K+ records analyzed Impact: - Enabled early detection of data inconsistencies - Helped planners validate inputs before forecasting - Improved trust in upstream data --- 4) Side Project (AI System) What I built: - AI-powered job assistant system Features: - Scrapes job postings - Scores relevance using LLMs - Generates tailored resume points and outreach messages - Tracks applications Tech: - FastAPI backend - LLM routing (cloud + local fallback) - SQLite storage Goal: - Build a system-driven workflow (not just model usage) --- My concern Most of my work sits at the intersection of: - forecasting - data systems - workflow automation I’m trying to move into: 👉 Applied Data Scientist / Product-oriented roles --- Questions 1. Does this profile look too niche (forecasting-heavy)? 2. Does “building systems around data” help or hurt for DS roles? 3. What’s the biggest gap you see (if any)? Would really appreciate honest feedback. Thanks.