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