Senior AI Engineer

Java Developer – Job Description

Wissen Technology is now hiring for a Java Developer - Bangalore with hands-on experience in Core Java, algorithms, data structures, multithreading and SQL.

We are solving complex technical problems in the industry and need talented software engineers to join our mission and be a part of a global software development team. A brilliant opportunity to become a part of a highly motivated and expert team which has made a mark as a high-end technical consulting.

Required Skills

• Experience: 4 to 7 years.
• Experience in Core Java and Spring Boot.
• Extensive experience in developing enterprise-scale applications and systems. Should possess good architectural knowledge and be aware of enterprise application design patterns.
• Should have the ability to analyze, design, develop and test complex, low-latency client facing applications.
• Good development experience with RDBMS.
• Good knowledge of multi-threading and high-performance server-side development.
• Basic working knowledge of Unix/Linux.
• Excellent problem solving and coding skills.
• Strong interpersonal, communication and analytical skills.
• Should have the ability to express their design ideas and thoughts.

About Wissen Technology

Wissen Technology is a niche global consulting and solutions company that brings unparalleled domain expertise in Banking and Finance, Telecom and Startups. Wissen Technology is a part of Wissen Group and was established in the year 2015.

Wissen has offices in the US, India, UK, Australia, Mexico, and Canada, with best-in-class infrastructure and development facilities. Wissen has successfully delivered projects worth $1 Billion for more than 25 of the Fortune 500 companies. The Wissen Group overall includes more than 4000 highly skilled professionals.

Wissen Technology provides exceptional value in mission critical projects for its clients, through thought leadership, ownership, and assured on-time deliveries that are always ‘first time right’.

Our team consists of 1200+ highly skilled professionals, with leadership and senior management executives who have graduated from Ivy League Universities like Wharton, MIT, IITs, IIMs, and NITs and with rich work experience in some of the biggest companies in the world.

Wissen Technology offers an array of services including:

• Application Development
• Artificial Intelligence & Machine Learning
• Big Data & Analytics
• Visualization & Business Intelligence
• Robotic Process Automation
• Cloud
• Mobility
• Agile & DevOps
• Quality Assurance & Test Automation

We have been certified as a Great Place to Work® for two consecutive years (2020–2022) and voted as the Top 20 AI/ML vendor by CIO Insider.

The AI Specialist candidate should have the following skills:

Must-have:

• Proficiency in Java
• Experience with LLMs, prompt engineering, and RAG architectures
• Familiarity with AWS cloud platform
• Strong analytical and problem-solving skills
• Ability to communicate complex technical concepts to non-technical stakeholders

About FrontM

At FrontM, we are on a mission to transform the lives of frontline workforces, particularly in the maritime industry. We believe in creating a more connected, empowered, and engaged workforce by building cutting-edge solutions that merge the power of technology with human-centric needs. Our vision is to develop the world’s leading digital toolbox platform for maritime operations —a platform that brings everything for frontline workforces from digital wallets, recruitment, onboarding, healthcare, and learning to welfare and human capital management under one seamless umbrella.

Role Summary

As a JavaScript Developer at FrontM, you will be at the forefront of developing our pioneering digital toolbox platform and the low-code developer framework that powers it. You will have the opportunity to work with the latest JavaScript frameworks, integrating advanced technologies such as Large Language Models (LLMs), AI, and the latest GPT models. You’ll also be part of our exciting roadmap to evolve our low-code platform into a no-code solution, making app development accessible to everyone. Your contributions will be pivotal in the creation and enhancement of the Maritime App Store, where innovation meets practicality, offering solutions that make a tangible difference in the lives of seafarers and other frontline workers.

Key Responsibilities

Application Development (≈60%)

• Build micro-apps using the frontm.ai framework
• Implement intent-based architectures, context and state management
• Develop responsive UIs, forms, collections, filters, and workflows
• Integrate AWS services (Lambda, S3, DynamoDB, Bedrock)
• Build conversational AI features and real-time capabilities (messaging, video, notifications)

Framework Development (≈25%)

• Enhance and extend the frontm.ai core framework
• Build reusable components, patterns, and accelerators
• Improve performance for low-bandwidth environments
• Contribute to documentation, examples, and design reviews
• Support migration towards TypeScript and future Rust components

AI-Assisted Development (≈15%)

• Use Claude Code for efficient development
• Write and refine prompts for code generation
• Review, validate, and harden AI-generated code
• Implement LLM integrations via AWS Bedrock / OpenAI
• Build AI assistants using the skills layer

Required Technical Skills

JavaScript / TypeScript

• 5+ years professional JavaScript experience
• Strong TypeScript, async patterns, modular design
• Clean code practices and modern tooling

Architecture & Cloud

• Microservices and event-driven systems
• Serverless AWS (Lambda, API Gateway, DynamoDB, S3)
• REST APIs, WebSockets, CI/CD
• Infrastructure as Code experience preferred

AI & LLMs

• Hands-on use of Claude Code or similar tools
• Prompt engineering and hallucination mitigation
• Conversational AI and NLP experience

Data

• MongoDB / MongoDB Atlas
• Caching, indexing, and multi-tenant data patterns

Desired skills

• Experience with low-bandwidth or offline-first systems
• Understanding of secure, distributed deployments
• Exposure to healthcare, logistics, or maritime systems

Experience & Education

• 5+ years software development
• 2+ years AWS serverless
• 1+ year AI-assisted development
• Degree in Computer Science or equivalent experience

Personal Attributes

• Strong problem-solving and critical thinking
• Comfortable reviewing AI-generated code
• Clear communicator and reliable team contributor
• Self-driven, detail-oriented, and adaptable

Why join FrontM?

Above-Market Compensation: We believe in rewarding talent, offering a salary package that reflects your skills and potential.

Long-Term Career Growth: As FrontM expands, so will your opportunities. We are committed to helping our team members develop their careers, offering mentorship, learning opportunities, and the chance to take on more responsibility.

Cutting-Edge Technology: Work with the latest in JavaScript frameworks, AI, LLMs, and GPT models, contributing to a platform that’s at the forefront of technological innovation.

Make a Real Impact: This is your chance to work on something that matters—to build solutions that directly improve the quality of life for thousands of people worldwide.

Job Title: AI Engineer

Location: Bengaluru 

Experience: 3 Years 

Working Days: 5 Days

About the Role

We’re reimagining how enterprises interact with documents and workflows—starting with BFSI and healthcare. Our AI-first platforms are transforming credit decisioning, document intelligence, and underwriting at scale. The focus is on Intelligent Document Processing (IDP), GenAI-powered analysis, and human-in-the-loop (HITL) automation to accelerate outcomes across lending, insurance, and compliance workflows.

As an AI Engineer, you’ll be part of a high-caliber engineering team building next-gen AI systems that:

• Power robust APIs and platforms used by underwriters, credit analysts, and financial institutions.
• Build and integrate GenAI agents.
• Enable “human-in-the-loop” workflows for high-assurance decisions in real-world conditions.

Key Responsibilities

• Build and optimize ML/DL models for document understanding, classification, and summarization.
• Apply LLMs and RAG techniques for validation, search, and question-answering tasks.
• Design and maintain data pipelines for structured and unstructured inputs (PDFs, OCR text, JSON, etc.).
• Package and deploy models as REST APIs or microservices in production environments.
• Collaborate with engineering teams to integrate models into existing products and workflows.
• Continuously monitor and retrain models to ensure reliability and performance.
• Stay updated on emerging AI frameworks, architectures, and open-source tools; propose improvements to internal systems.

Required Skills & Experience

• 2–5 years of hands-on experience in AI/ML model development, fine-tuning, and building ML solutions.
• Strong Python proficiency with libraries such as NumPy, Pandas, scikit-learn, PyTorch, or TensorFlow.
• Solid understanding of transformers, embeddings, and NLP pipelines.
• Experience working with LLMs (OpenAI, Claude, Gemini, etc.) and frameworks like LangChain.
• Exposure to OCR, document parsing, and unstructured text analytics.
• Familiarity with model serving, APIs, and microservice architectures (FastAPI, Flask).
• Working knowledge of Docker, cloud environments (AWS/GCP/Azure), and CI/CD pipelines.
• Strong grasp of data preprocessing, evaluation metrics, and model validation workflows.
• Excellent problem-solving ability, structured thinking, and clean, production-ready coding practices.

About the role

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities

• Build and iterate on prompt pipelines and multi-agent workflow components
• Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
• Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
• Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
• Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
• Debug and maintain pipeline stages in production

What you bring:  

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
• Solid Python fundamentals clean, working, readable code
• Hands-on experience with LLM APIs and prompt engineering (personal projects count)
• Comfort with Git, REST APIs, and working in a Linux environment
• A feel for content and narrative you can judge whether generated output is actually good, not just valid
• Curiosity and clear communication you ask good questions and don't stay stuck silently

 Preferred

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
• Experience writing evals or LLM-as-judge scoring
• Node.js and Fastapi familiarity, or experience deploying on AWS

We’re building financial data systems that power real business decisions — and we need someone who knows how to make Django, PostgreSQL, Celery, and APIs play nicely at scale.

You know what clean, reliable backend systems look like — because you’ve built them. You’ve handled authentication quirks, rate limits, and webhooks; you build code that’s maintainable, performant, and correct.

If you’re curious about financial data, transactions, and KPIs, and enjoy turning numbers into insight, this role is for you. (Experience with banking or accounting APIs is a big plus.)

What You’ll Do

• Build and maintain Django-based systems using Celery for async and scheduled tasks
• Integrate and manage data from multiple APIs (banking, accounting, analytics, etc.)
• Design and optimize internal APIs
• Improve reliability, scalability, and performance
• Collaborate closely with product and frontend engineers

Your Profile

You’re an experienced backend engineer who is strong in all core areas of modern Python development:

• Python, Django, Django REST Framework
• Celery and asynchronous / distributed task handling
• PostgreSQL and robust data modeling
• Docker and containerized deployment workflows
• Unit testing (pytest or equivalent) and maintainable test structures
• Git, CI/CD, and reliable deployment automation
• API integrations (REST, OAuth, webhooks)
• Caching strategies & background job handling (preferred Redis experience)
• Solid understanding of security, permissions, and performance optimization

You’ve already built or scaled production-grade systems and can take ownership from architecture to deployment.

Experience with fintech or data integration systems is an advantage.

How We Work

We’re a small, remote-first team that values ownership over micromanagement.

You’ll have freedom in how you work — what matters is your impact.

Our daily language is English.

About finban

We’re building a platform that helps businesses understand and manage their financial performance in real time by connecting and analyzing data from banking, accounting, and analytics systems.

If you’ve built or scaled production Django systems before and love clean architecture — we’d love to see your work.

Experience: 8+ years, senior candidates only | Type: Full-time | Location: Remote (India)

 ---

 WHAT WE'RE BUILDING

 See http://www.juliet.space

 We're building Juliet, an AI that runs marketing end to end. Our users are marketers, founders, CEOs, growth leads, agencies, and SMBs — not developers. They

 tell Juliet the goal. She plans, writes production code, and ships real marketing: conversion-optimized websites, launch assets, campaigns, audits, autonomously.

 That's the engineering problem in one line: the humans in the loop can't read code, so the agent has to get it right on her own — plan, build, self-correct,

 recover, ship.

 Under the hood: a browser-based studio backed by cloud sandboxes, a real-time SSE streaming pipeline, and a LangGraph agent working across 83 tools and 63 skill

 modules. The agent isn't bolted onto the product. She is the product.

 Small team, big ambitions. You'll ship things users touch daily, not write tickets about them.

 ---

 THE ROLE

 We're hiring one architect-level backend engineer to own Juliet's agentic infrastructure end to end. That means the agent graph, the execution environment, the

 streaming pipeline, the state and memory systems — and setting technical direction for the engineers working alongside you.

 This is a player-coach seat. You'll still write code every day, and your architectural calls become the product. You'll work directly with the founder. No PMs in

 between.

 Frontend is part of the system. You won't be leading it, but you'll need to understand how the agent's output reaches the browser and be able to ship full-stack

 features when needed.

 ---

 THE STACK

 AI agent (primary): Python 3.11, LangGraph 1.x + LangChain, Anthropic / Google / OpenAI model providers

 API (primary): NestJS 11, Supabase, Redis, PostgreSQL, Server-Sent Events

 Infra (primary): Modal cloud sandboxes, Docker, Netlify deployments

 Frontend (secondary): Next.js 15, React 19, TypeScript, Zustand, CodeMirror 6, XTerm.js

 Monorepo: Turborepo, pnpm

 ---

 WHAT YOU'LL WORK ON

 The majority of your time is here:

 Agentic AI workflows — Design, extend, and harden the LangGraph agent graph: multi-step planning, code generation, tool dispatch, self-correction, and recovery

 across 83 tools and 63 skill modules. This is the core of the product.

 Real-time streaming architecture — The SSE pipeline that carries every agent action from the Python backend through NestJS to the browser: event framing,

 reconnection, health monitoring, interrupt handling for plan approvals and clarifying questions.

 Agent execution environments — Sandbox lifecycle on Modal: container spin-up, file sync, terminal I/O, command execution, and live preview with per-asset esbuild

 bundling. The agent lives here.

 State and memory systems — LangGraph Postgres checkpointers, middleware-injected context (goals, design docs, memory anchors), conversation summarization. How

 the agent knows what it knows.

 Backend API and data layer — NestJS services, Supabase schema, Redis caching, quota enforcement, webhook handling. The plumbing the agent depends on.

 Marketing intelligence pipelines — AEO, CRO, and brand-perception audit engines: multi-LLM probing, parallel inference, streamed structured reports, result

 caching. Audit-at-scale infrastructure.

 The remaining ~25% of your time:

 Full-stack product features — Collaboration (roles and permissions), the Netlify deployment pipeline, subscription and quota flows, onboarding. You'll ship these

 end to end — backend first, frontend to close the loop.

 ---

 WHAT WE'RE LOOKING FOR

 Must-have:

 - 8+ years of professional software engineering, including meaningful time as a tech lead or systems architect who owned something end to end. Closer to ten is

 the norm for people who thrive here.

 - Both worlds on your resume: engineering rigor inside a large company and 0-to-1 ownership at an early-stage startup.

 - Production agentic systems experience. You've built and operated LLM agent systems in production with LangGraph, LangChain, or equivalent — agent graphs, tool

 use, state management, prompt engineering, evals. This means well beyond calling a chat endpoint.

 - Strong Python. You design and ship production Python daily. The agent codebase is yours to own.

 - Architect-level system design. You can own how data flows across four services, make tradeoffs under uncertainty, and defend every call.

 - AI-native development workflow. You drive Claude Code, Codex, or similar agentic tools as everyday instruments — not occasionally. You have opinions about

 working with coding agents because you do it constantly.

 - Real-time backend systems. You've built SSE, WebSocket, or streaming API infrastructure in production — not just consumed it.

 - Strong TypeScript. The API layer and most product features are in TypeScript. You're productive in it.

 Strong plus:

 - Background in developer tools, IDEs, or coding/execution platforms

 - Container runtimes and sandboxed execution (Modal, E2B, Firecracker, or similar)

 - Depth in PostgreSQL, Redis, and Supabase

 - LLM observability and evals tooling (LangSmith or similar)

 - NestJS or equivalent Node.js API framework experience

 - React/Next.js — enough to ship a full-stack feature without handoff

 - Exposure to marketing, growth, or publisher-facing products

 ---

 WHY THIS ROLE IS DIFFERENT

 You own the architecture. Not a feature factory. Not someone else's design doc. The technical execution of an AI product is yours to lead.

 The agent is the product. You're not adding AI to an existing system. You're building and operating the system that is the AI. Every architectural decision

 touches what Juliet can and can't do.

 Hard problems, always. The system spans cloud sandboxes, streaming infrastructure, multi-step agent graphs, and a full-stack web product — for non-technical

 users who can't course-correct a broken output. The bar is high.

 Small team, real leverage. Your code ships to users the same week. No layers of approval.

 ---

 HOW TO APPLY

 Send us:

 1. A short note on the most complex agentic system you've shipped: what broke, and what you'd redo. A link to something you've built that involves agent graphs, tool use, or autonomous multi-step execution

 2. What is one thing you would improve about Juliet? It could be a feature or a bug.

Experience: 5+ years production software engineering, with 2+ years working directly on LLM or agent systems in production. 

Location: Remote 

To streamline and fast-track screening, please submit your details here (if you haven’t already): https://airtable.com/appbtkr4odapnb5I6/pagqo91lKv3VJg3GT/form 

We’ll review your responses as part of the initial screening process. Please make sure you complete and submit all details through the form to be considered for the next stage. Submissions outside the form may not be considered.

Why This Role Matters

Terrabase builds agent infrastructure that enterprise customers rely on daily for SQL generation, forecasting, data analysis, and artifact delivery. Our orchestration layer routes between specialized sub-agents, manages typed handoff contracts, runs structured eval suites, and enforces correctness across every turn.

This is not a research-prototype role. You will build and evolve agent architecture, but always in service of making the system observable, typed, evaluated, recoverable, and boringly reliable in production.

What You Will Do

Own the harness architecture and middleware stack. Our LangGraph orchestrator routes between sub-agents through a layered middleware stack: file upload handling, source resolution, local context, workspace sync, state hydration, aggregation barriers, and typed handoff contracts. You will extend this stack, enforce its contracts in code, and keep it operational as routing logic and agent surfaces evolve.

Maintain typed contracts and boundaries. Agent handoffs at Terrabase carry typed contracts with barrier conditions and retry predicates. You will design these contracts, enforce them with strict typing, manage backward compatibility when contracts change, and write the contract tests that prevent silent regressions.

Own the eval suites. We run structured eval suites across routing decisions, context-resolution accuracy, multi-turn coherence, visual reference alignment, and artifact correctness. You will extend coverage, write new evals where gaps exist, and build CI gates that block releases when regressions are detected. A routing change or prompt change with no eval coverage does not ship.

Triage production failures and close the loop. When an agent turn fails in production, you will trace it in LangSmith, identify the failure class, and convert it into a durable regression test. You will own the release gates, keep prompts and runtime contracts in sync, manage feature flag rollout risk, and remove dead paths as the system evolves.

Own SQL and artifact correctness. Our agents generate SQL over customer schemas and produce structured artifacts (reports, dashboards, data sheets) under a strict schema contract. You will own the correctness layer: source grounding, schema-aware validation, provenance surfaces, and the eval infrastructure that catches generated artifact failures before they reach customers.

Build and maintain HITL workflows. Human-in-the-loop checkpoints let users intervene, redirect, or approve mid-chain. You will design these workflows, enforce their resumable state contracts, and ensure they degrade gracefully when interrupted.

Instrument for traceability. You will extend LangSmith tracing coverage, add structured span annotations, and build the tooling that lets us diagnose a bad agent turn from production trace data alone, without requiring a local reproduction.

What We Are Looking For

• 5+ years production software engineering, with strong Python fundamentals
• 2+ years working hands-on with LLM-based systems: agent loops, tool use, context management, or inference pipelines
• Experience with LangGraph, LangChain, OpenAI/Anthropic tool-use systems, or equivalent multi-step agent/runtime orchestration
• Practical eval engineering: you have built or extended eval harnesses, written automated test cases for agent behavior, and treated evaluation as an ongoing engineering discipline
• Strong engineering hygiene: strict typing, small interfaces, contract tests, clear schema migrations, and CI discipline
• Ability to debug from production traces and artifacts, not only local reproductions
• Comfort working across prompts, Python runtime code, TypeScript product surfaces, data systems, and eval infrastructure
• Systems thinking: you design for observability, recovery, and state management, not just the happy path
• Maintenance ownership mindset: you triage, close loops, and leave systems more debuggable than you found them
• Pragmatic judgment: you can distinguish between reliability-critical infrastructure and speculative abstraction

Bonus Points

• HITL workflow design: checkpoints, approvals, mid-chain interrupts, resumable state
• Context engineering depth: chunking strategies, retrieval-augmented generation, semantic routing, re-ranking
• Experience with LangSmith, Weights and Biases, or similar trace and evaluation platforms
• Prior work shipping agent systems to enterprise customers where SQL or data correctness is a hard requirement
• Experience with mypy, Pydantic contracts, or strict typing disciplines in a production Python codebase

Life at Terrabase

We are a sharp, focused, fully remote team building agent infrastructure that enterprise customers trust with their data. You will work directly alongside the engineer who designed this harness, with broad ownership, generous compute budgets, and a culture that treats reliability as a product requirement, not a research topic.

Terrabase is an equal-opportunity employer. We celebrate diversity and are committed to building an inclusive environment for every team member.

What you’ll be doing

 Weare much more than our job descriptions, but here is where you will begin:

 As a Senior Software Engineer Data & ML You’ll Be:

 ● Architect, design, test, implement, deploy, monitor and maintain end-to-end backend

 services. You build it, you own it.

 ● Work with people from other teams and departments on a day to day basis to ensure

 efficient project execution with a focus on delivering value to our members.

 ● Regularly aligning your team’s vision and roadmap with the target architecture within your

 domain and to ensure the success of complex multi domain initiatives.

 ● Integrate already trained ML and GenAI models (preferably GCP in services.

ROLE:

 Whatyou’ll need,

 Like us, you’ll be deeply committed to delivering impactful outcomes for customers.

 What Makes You a Great Fit

 ● 5 years of proven work experience as a Backend Python Engineer

 ● Understanding of software engineering fundamentals OOPS, SOLID, etc.)

 ● Hands-on experience with Python libraries like Pandas, NumPy, Scikit-learn,

 Lang chain/LLamaIndex etc.

 ● Experience with machine learning frameworks such as PyTorch or TensorFlow, Keras, being

 proficient in Python

 ● Hands-on Experience with frameworks such as Django or FastAPI or Flask

 ● Hands-on experience with MySQL, MongoDB, Redis and BigQuery (or equivalents)

 ● Extensive experience integrating with or creating REST APIs

 ● Experience with creating and maintaining CI/CD pipelines- we use GitHub Actions.

 ● Experience with event-driven architectures like Kafka, RabbitMq or equivalents.

 ● Knowledge about:

 o LLMs

 o Vector stores/databases

 o PromptEngineering

 o Embeddings and their implementations

 ● Somehands-onexperience in implementations of the above ML/AI will be preferred

 ● Experience with GCP/AWS services.

 ● You are curious about and motivated by the future trends in data, AI/ML, analytics

We are looking for a Technical Lead - GenAI with a strong foundation in Python, Data Analytics, Data Science or Data Engineering, system design, and practical experience in building and deploying Agentic Generative AI systems. The ideal candidate is passionate about solving complex problems using LLMs, understands the architecture of modern AI agent frameworks like LangChain/LangGraph, and can deliver scalable, cloud-native back-end services with a GenAI focus.

Key Responsibilities :

- Design and implement robust, scalable back-end systems for GenAI agent-based platforms.

- Work closely with AI researchers and front-end teams to integrate LLMs and agentic workflows into production services.

- Develop and maintain services using Python (FastAPI/Django/Flask), with best practices in modularity and performance.

- Leverage and extend frameworks like LangChain, LangGraph, and similar to orchestrate tool-augmented AI agents.

- Design and deploy systems in Azure Cloud, including usage of serverless functions, Kubernetes, and scalable data services.

- Build and maintain event-driven / streaming architectures using Kafka, Event Hubs, or other messaging frameworks.

- Implement inter-service communication using gRPC and REST.

- Contribute to architectural discussions, especially around distributed systems, data flow, and fault tolerance.

Required Skills & Qualifications :

- Strong hands-on back-end development experience in Python along with Data Analytics or Data Science.

- Strong track record on platforms like LeetCode or in real-world algorithmic/system problem-solving.

- Deep knowledge of at least one Python web framework (e.g., FastAPI, Flask, Django).

- Solid understanding of LangChain, LangGraph, or equivalent LLM agent orchestration tools.

- 2+ years of hands-on experience in Generative AI systems and LLM-based platforms.

- Proven experience with system architecture, distributed systems, and microservices.

- Strong familiarity with Any Cloud infrastructure and deployment practices.

- Should know about any Data Engineering or Analytics expertise (Preferred) e.g. Azure Data Factory, Snowflake, Databricks, ETL tools Talend, Informatica or Power BI, Tableau, Data modelling, Datawarehouse development.