AI Agent Developer

Location: Remote (with Bangalore Data Science team)
Experience Required: 5+ years
Contract Type: Full-time
Salary - ₹18-28 LPA dependent on experience
About Zavmo Zavmo, your lifelong learning companion, is an AI-powered learning platform transforming professional development through intelligent personalisation.

We've built an 18-agent multi-agent system that delivers the 4D Process (Discover, Discuss, Deliver, Demonstrate), integrating National Occupational Standards, OFQUAL frameworks, and career progression intelligence.
Recently recognised as Top 20 AI Innovation Learning Tools by Training Magazine (2025).
The Role We need an experienced AI engineer who can build and enhance specialised agents within our production multi-agent architecture.

You'll be working with a 4-person Data Science team in Bangalore, implementing RAG-based agents that handle everything from role matching and career progression to assessment and quality assurance.
This isn't a research role - we have clear specifications.

You'll be shipping production code that serves real learners pursuing regulated qualifications.
What You'll Build Immediate Projects:
Agent #3: Career Progression Intelligence Agent (RAG + pathway ranking algorithms)
Agent #16: Learning Specification Triangulation Agent (LO/LO/AC alignment validation)
Agent #17: Assessor Agent (formative/summative assessment with PASS/REFER/FAIL logic)
Agent #18: Quality Assurance Agent (meta-agent for self-critique across all agents)
Enhancement Work:
Expand Agent #1 (Role Matching) to handle 5,000+ job descriptions across 23 sectors
Enhance Agent #8 (Content Recommendation) with job description contextualisation
Integrate Agent #10 (Conversational Facilitator) with role-specific examples
Core Technical Stack (Must Have 5+ Years) Primary Technologies:
Python - All agent logic, RAG implementation, API development
Vector Databases - Pinecone, Weaviate, Qdrant, or ChromaDB (operational production experience)
LLM Integration - Anthropic Claude API, prompt engineering, structured outputs
RAG Architecture - Retrieval Augmented Generation, semantic search, hybrid search
Embedding Models - OpenAI, Cohere, or Sentence Transformers (generation and vector operations)
Essential Skills:
API design and RESTful services
JSON/data structure design
Database schema design and migrations
Integration testing and validation
Error handling and system reliability
Required Experience You Must Have:
5+ years Python development in production environments
2+ years working with vector databases in production
2+ years building RAG-based systems or LLM applications
Proven experience with embedding generation and semantic search
Experience designing and implementing APIs for agent-based systems
Strong understanding of prompt engineering and LLM behaviour
Ideally You Also Have:
Experience with multi-agent systems or agent orchestration
Background in ML classification (scikit-learn, PyTorch)
Knowledge of hybrid search algorithms (vector + keyword)
Experience with xAPI or learning analytics standards
Understanding of UK education frameworks (NOS, OFQUAL) - nice to have
HR/L&D domain knowledge - nice to have
What We're Not Looking For This role isn't suitable if you:
Need extensive training on Python, vectors, or RAG fundamentals
Haven't worked with production LLM applications before
Don't have hands-on vector database experience
Primarily work in research rather than production systems
Need close supervision to implement clearly specified features
The Team You'll work with:
4 Data Scientists in Bangalore - Your immediate team (DS1-DS4 structure)
Juliette Denny - Founder, architect of the 4D Process and agent specifications
Cosmo (CTO) - ADHD brain like a solar system, technical architecture lead
Gerard (Chairman) - Strategic oversight
How We Work Development Approach:
Clear specifications with detailed architecture documents
ReACT pattern (Reasoning → Action → Critique) for all agents
Work packages with defined deliverables and acceptance criteria
Parallel workstreams across the 4-person DS team
Regular integration testing between agents
Current Sprint Example:
B1: Agent #3 Core Logic (DS3, 7 days)
B2: Integration Layer + Agent #16, #18 (DS4, 5 days)
B3: Enhanced Agent #1 (DS3, 3 days)
B4: Enhanced Agent #8 + #10 (DS4, 3 days)
Timeline: 3-4 week sprints, production deployments
Technical Environment Data Infrastructure:
5,000+ vectorised job descriptions across 23 sectors
NOS Database (vectorised)
OFQUAL Register (vectorised)
Career Pathways Database (500+ progression routes)
User Profile Database (operational)
Performance Requirements:
Response times 95%+ accuracy on role matching
Semantic consistency across all vector operations
Complete audit trails for OFQUAL compliance
Why This Role Matters You'll be building AI that genuinely helps people progress in their careers.

Not chatbots that hallucinate.

Not marketing demos.

Real production systems that:
Match learners to 5,000+ career roles with 95% accuracy
Map progression pathways with skill gap analysis
Deliver regulated qualifications (OFQUAL-compliant)
Provide formal assessments with human oversight
Your code will serve hundreds of thousands of learners globally, helping them live their ideal life design.
What We Offer Competitive salary (based on experience)
Remote-first culture
Work with cutting-edge AI technology in production
Direct impact on people's career progression
Collaborative team that ships real products
Opportunity to work across the full agent architecture