Job Overview
| Particulars | Details |
|---|---|
| Position | Agentic AI Engineer / Lead (RAG + LLM Systems) |
| Company | Codvo.ai |
| Job Type | Remote |
| Experience | 4 - 10 years |
| Category / Department | IT |
| Application Mode |
🏢 About Codvo.ai
Codvo.ai is an industry-focused Enterprise AI company helping organizations transform Artificial Intelligence from isolated proofs of concept into secure, governed, and scalable operational systems that deliver measurable business outcomes.
The company designs, deploys, governs, and manages enterprise-grade AI workers and AI models capable of executing business operations safely, efficiently, and reliably. Through its proprietary AI execution platform, NeIO, Codvo.ai enables organizations to connect enterprise systems, policies, approvals, monitoring, auditability, and operational workflows into a unified AI ecosystem.
Codvo.ai focuses on delivering enterprise AI solutions that improve operational efficiency, reduce costs, increase quality, strengthen compliance, and provide complete business visibility.
🌟 Company Highlights
• Industry-Focused Enterprise AI Partner
• AI Workers & Agentic AI Systems
• Enterprise AI Governance Platform
• Secure & Compliant AI Operations
• Production-Grade AI Delivery
• AI Execution Control Plane (NeIO)
🏆 Awards & Certifications
• Great Place to Work® Certified
• ISO 27001 Certified
• SOC 2 Type II Certified
• Strategic Technology Partner Awards
🎯 Mission
Helping enterprises transform AI into governed operational capabilities that drive measurable business outcomes.
🏢 Company Headquarters
Codvo.ai Headquarters
6900 Dallas Parkway
Plano, Texas 75024-7144
United States
💡 Editor's Note / Preparation Tip
For Agentic AI roles, recruiters primarily evaluate your ability to design, deploy, monitor, and scale enterprise AI systems rather than simply your familiarity with AI frameworks.
✅ Resume Preparation Checklist
• Highlight production RAG implementations.
• Mention Agentic AI or multi-agent projects.
• Include LangChain, LangGraph, CrewAI, or similar frameworks.
• Showcase vector database implementations.
• Include LLMOps, monitoring, and observability experience.
• Add cloud deployment experience.
• Include GitHub repositories, technical blogs, or AI portfolios.
• Quantify business outcomes and performance improvements.
🎯 Interview Preparation Topics
Generative AI
• Large Language Models
• Prompt Engineering
• Function Calling
• Tool Usage
• Context Management
Agentic AI
• AI Agents
• Multi-Agent Systems
• Agent Communication
• LangGraph
• CrewAI
• Workflow Orchestration
RAG
• Retrieval Architecture
• Embeddings
• Chunking Strategies
• Vector Search
• Reranking
• Hybrid Search
Vector Databases
• FAISS
• Pinecone
• Weaviate
• pgvector
Backend Engineering
• Python
• FastAPI
• APIs
• Microservices
• Async Programming
LLMOps
• Evaluation Frameworks
• Monitoring
• Observability
• Hallucination Detection
• Performance Optimization
Cloud & Infrastructure
• AWS
• Azure
• GCP
• Docker
• Kubernetes
💡 Frequently Asked Interview Questions
• Explain the architecture of a production RAG system.
• How would you design an enterprise Agentic AI platform?
• What are the differences between LangChain and LangGraph?
• How do you evaluate LLM performance?
• How do you reduce hallucinations?
• Explain vector embeddings and semantic search.
• Describe your LLMOps implementation.
• How do multiple AI agents communicate?
• Explain AI observability and monitoring.
• Describe an enterprise AI system you designed and deployed.
📌 Application Tips
• Emphasize production AI implementations.
• Highlight system architecture responsibilities.
• Include measurable business impact.
• Add links to GitHub, portfolio, or technical articles.
• Clearly mention your notice period.
• Ensure your resume is ATS-friendly.
🚀 Pro Tip
Candidates who can explain complete enterprise AI architecture—including data ingestion, embeddings, retrieval, orchestration, agent communication, LLM evaluation, observability, governance, security, and production deployment—typically perform exceptionally well in senior Agentic AI interviews.
📝 Job Description
🚀 Codvo.ai Hiring | Agentic AI Engineer / Lead (RAG + LLM Systems) | Remote India | Generative AI Jobs
🏢 Company: Codvo.ai
💼 Position: Agentic AI Engineer / Lead (RAG + LLM Systems)
📌 Department: Artificial Intelligence / Generative AI / Enterprise AI Engineering
📍 Location: Remote (India)
🧑💻 Experience: 4–10 Years
⚡ Notice Period: Immediate to 30 Days
🎓 Qualification: B.E. / B.Tech / MCA / M.Tech / Computer Science, AI, Data Science, or related field
⏳ Employment Type: Full-Time
📧 Application Mode: Email Application
💼 Job Details
Position: Agentic AI Engineer / Lead (RAG + LLM Systems)
Experience: 4–10 Years
Location: Remote (India)
Notice Period: Immediate to 30 Days
Employment Type: Full-Time
⭐ Must-Have Technologies
• Python
• FastAPI
• LangChain
• LangGraph
• Agentic AI Frameworks
• RAG Pipelines
• LLM Integrations
• Vector Embeddings
• Enterprise APIs
• Microservices
🛠 Tech Stack
Programming
• Python
Backend
• FastAPI
• REST APIs
• Microservices
AI Frameworks
• LangChain
• LangGraph
• Agentic AI Frameworks
AI Systems
• LLMs
• RAG
• AI Agents
Vector Databases
• FAISS
• Pinecone
• Weaviate
Cloud Platforms
• AWS
• Microsoft Azure
• Google Cloud Platform
AI Operations
• LLMOps
• Monitoring
• Evaluation
• Observability
🎯 What We're Looking For
• Passion for Generative AI and Agentic AI
• Strong system design and architecture skills
• Experience building production AI platforms
• Excellent analytical and problem-solving abilities
• Ownership mindset and leadership qualities
• Strong communication and collaboration skills
• Ability to work in a fast-paced, innovation-driven environment
🌟 Why Join Codvo.ai?
• Build enterprise-scale Agentic AI platforms
• Work on cutting-edge LLM technologies
• Exposure to production-grade AI governance systems
• Opportunity to shape the future of Enterprise AI
• Remote-first work environment
• Work with experienced AI architects and engineers
• Solve real-world enterprise automation challenges
• Excellent career growth opportunities
📩 How to Apply
Interested candidates can share their updated resume at:
📢 Note
Only shortlisted candidates will be contacted for the further recruitment process.
🎯 Key Responsibilities
- ✓ Design and build enterprise-grade Agentic AI systems
- ✓ Develop production-scale Retrieval-Augmented Generation (RAG) pipelines
- ✓ Architect and implement multi-agent workflows
- ✓ Build scalable LLM-powered enterprise applications
- ✓ Develop AI orchestration frameworks and AI agents
- ✓ Design and implement LLMOps pipelines
- ✓ Build AI evaluation, observability, and monitoring systems
- ✓ Develop scalable APIs and microservices using Python and FastAPI
- ✓ Integrate enterprise data sources and external systems
- ✓ Deploy and optimize AI workloads on cloud platforms
- ✓ Implement enterprise-grade AI governance and monitoring
💡 Required Qualifications & Skills
🚀 Career Guidance & Interview Insights
To help you succeed, we've compiled original preparation guides, resume keywords, and growth analytics for this category of role.
Joining as a Agentic AI Engineer / Lead (RAG + LLM Systems) offers a fantastic opportunity to work at the forefront of technological innovation. You will be exposed to modern software development lifecycles, collaborate with cross-functional product teams, and design scalable architectures that directly impact end-users. The continuous learning curve in tech ensures that your programming, debugging, and system engineering skills remain highly marketable and sharp.
The career roadmap in technology is highly rewarding. Typical progression starts as a Junior Software Engineer, moving to Associate Developer and then Senior Software Engineer (3-6 years). From there, professionals choose between an individual contributor track—becoming a Tech Lead, Staff Engineer, and Principal Architect—or a management track, advancing to Engineering Manager, Director of Engineering, and Chief Technology Officer (CTO).
Expected Technical & Behavioral Questions:
- Q1: How do you optimize database query performance?
Answer Tip: Talk about indexing strategies, query execution plan analysis, avoiding N+1 queries, and implementing caching layers (like Redis). - Q2: Explain the differences between Microservices and Monolithic architecture.
Answer Tip: Highlight scaling advantages, loose coupling, independent deployments, network latency considerations, and data consistency challenges in microservices. - Q3: How do you handle merge conflicts in Git?
Answer Tip: Explain checking out the local branch, pulling the latest main branch, running git merge, opening the conflicted files to resolve blocks manually, and committing the resolved merge.
To pass automated ATS (Applicant Tracking System) screening and catch the recruiter's eye, tailor your resume with the following tips:
- Keywords to include: Software Engineering, SDLC, OOP, CI/CD Pipelines, SQL databases, Agile, Unit Testing, Code Review, Cloud Architectures (AWS/Azure).
- Format: Use a clean, single-column resume format. Avoid graphics, text boxes, or tables which can scramble ATS parser outputs.
- Quantify Results: Instead of writing 'wrote code', write 'Developed a caching system that reduced server response times by 35%.'
Estimated compensation for this role type in India is ₹6,00,000 - ₹12,00,000 per annum (Mid level). The actual salary package offered depends on factors such as company size, work mode (remote or on-site), individual technical proficiency, and negotiations during final HR rounds.
Highly recommended certifications include: AWS Certified Solutions Architect, Microsoft Certified: Azure Fundamentals, Certified Kubernetes Administrator (CKA), Oracle Certified Professional Java SE, or tech-stack specific achievements (e.g. Meta Front-End Developer certification).
Frequently Asked Questions
Q1: Is this a remote position?
A: Yes, this is a fully remote opportunity for candidates located in India.
Q2: What experience level is required?
A: Candidates with 4–10 years of relevant experience are eligible.
Q3: Is Agentic AI experience mandatory?
A: Yes, hands-on experience with Agentic AI systems and RAG pipelines is required.
Q4: Which cloud platforms are preferred?
A: Experience with AWS, Azure, or GCP is preferred.
Q5: What notice period is acceptable?
A: Immediate joiners to candidates serving up to 30 days' notice period are preferred.
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