a Bit About Us
Appknox is one of the top Mobile Application security companies recognized by Gartner and G2. A profitable B2B SaaS startup headquartered in Singapore & working from Bengaluru.The primary goal of Appknox is to help businesses and mobile developers secure their mobile applications with a focus on delivery speed and high-quality security audits.Appknox has helped secure mobile apps at Fortune 500 companies with major brands spread across regions like India, South-East Asia, Middle-East, US, and expanding rapidly. We have secured 300+ Enterprises globally.We are a 60+ incredibly passionate team working to make an impact and helping some of the biggest companies globally. We work in a highly collaborative, very fast-paced work environment. If you have what it takes to be part of the team, we are excited and let’s speak further.
The Opportunity
Appknox AI
is building next-generation AI-powered security analysis tools for mobile applications. We use multi-agent systems and large language models to automate complex security workflows that traditionally require manual expert analysis.We're looking for an
AI/ML Engineer
who will focus on improving our AI system quality, optimizing prompts, and building evaluation frameworks. You'll work with our engineering team to make our AI systems more accurate, efficient, and reliable.This is NOT a data scientist role. We need someone who builds production AI systems with LLMs and agent frameworks.
Key Focus
Primary Focus: AI System Quality
- Prompt Engineering: Design and optimize prompts for complex reasoning tasks
- Quality Improvement: Reduce false positives and improve accuracy of AI-generated outputs
- Evaluation Frameworks: Build systems to measure and monitor AI quality metrics
- Tool Development: Create utilities and tools that enhance AI capabilities
Secondary Focus: Performance & Optimization
- Cost Optimization: Implement strategies to reduce LLM API costs (caching, batching, model selection)
- Metrics & Monitoring: Track system performance, latency, accuracy, and cost
- Research & Experimentation: Evaluate new models and approaches
- Documentation: Create best practices and guidelines for the team
Requirements
- 2-4 years of professional software engineering experience with Python as primary language
- 1+ years working with LangChain, LangGraph, or similar agent frameworks (AutoGPT, CrewAI, etc.)
- Production LLM experience: You've shipped products using OpenAI, Anthropic, Google Gemini, or similar APIs
- Prompt engineering skills: You understand how to structure prompts for complex multi-step reasoning
- Strong Python: Async/await, type hints, Pydantic, modern Python practices
- Problem-solving mindset: You debug systematically and iterate based on data
Good To Have Skill-set
- Experience with vector search (LanceDB, Pinecone, Weaviate, Qdrant)
- Knowledge of retrieval-augmented generation (RAG) patterns
- Background in security or mobile application development
- Understanding of static/dynamic analysis tools
What We're NOT Looking For
- Only academic/tutorial LLM experience (we need production systems)
- Pure ML research focus (we're not training foundation models)
- Data analyst/BI background without engineering depth
- No experience with LLM APIs or agent frameworks
AI/ML Infrastructure
Our Tech Stack:
- Agent Frameworks: LangChain, LangGraph
- LLMs: Google Gemini (primary), with multi-model support
- Observability: Langfuse, DeepEval
- Vector Search: LanceDB, Tantivy
- Embeddings: Hybrid approach (local + cloud APIs)
Platform & Infrastructure
- Orchestration: Prefect 3.x, Docker, Kubernetes
- Storage: S3-compatible object storage, PostgreSQL
- Languages: Python 3.11+
- Testing: pytest with parallel execution support
Work Expectations & Success Metrics
Within 1 Month (Onboarding)
- Understand AI system architecture and workflows
- Review existing prompts and evaluation methods
- Run analyses and identify improvement areas
- Collaborate on initial optimizations
Within 3 Months (Initial Contributions)
- Own prompt engineering for specific components
- Build evaluation datasets and quality metrics
- Implement tools that extend AI capabilities
- Contribute to performance optimization experiments
Within 6 Months (Independent Ownership)
- Lead quality metrics implementation and monitoring
- Drive prompt optimization initiatives
- Improve evaluation frameworks
- Research and prototype new capabilities
Within 1 Year (Expanded Scope)
- Mentor team members on best practices
- Lead optimization projects (caching, batching, cost reduction)
- Influence architectural decisions
- Build reusable libraries and internal frameworks
Interview Process
- Round 0 Interview - Profile Evaluation (15 min)
- Round 1 Interview - Take Home Assignment
- Round 2 Interview - Technical Deep-Dive (90 min)
- Round 3 Interview- Team Fit (45 min)
- Round 4 Interview- HR Round ( 30 min)
Why Join Appknox AI?
Impact & Growth
Work on cutting-edge AI agent systems that power real-world enterprise security. You’ll collaborate with experienced engineers across AI, security, and infrastructure while gaining deep expertise in LangGraph, agent systems, and prompt engineering. As we scale, you’ll have clear opportunities to grow into senior and staff-level roles.
Team & Culture
Join a small, focused product-engineering team that values code quality, collaboration, and knowledge sharing. We’re in a hybrid set-up - based out of Banaglore, flexible, and committed to a sustainable pace - no crunch, no chaos.
Technology
Built with a modern Python stack (3.11+, async, type hints, Pydantic) and the latest AI/ML tools including LangChain, LangGraph, DeepEval, and Langfuse. Ship production-grade features that make a real impact for customers.
Compensation & Benefits
Competitive Package
We offer strong compensation designed to reward impact.
Flexibility & Lifestyle
Hybrid work setup and enjoy generous time off. You’ll get top-tier hardware and the tools you need to do your best work.
Learning & Development
Access a substantial learning budget, attend major AI/ML conferences, explore new approaches during dedicated research time, and share your knowledge with the team.
Health & Wellness
Comprehensive health coverage, fitness subscription, and family-friendly policies.
Early-Stage Advantages
Help shape the culture, influence product direction, and work directly with founders. Move fast, ship quickly, and see your impact immediately.Skills:- Python, LangChain, LLMs, Retrieval Augmented Generation (RAG) and Prompt engineering
