DCKAP

Goals are great, but systems get the work done. DCKAP makes it easier for systems to work with each other. We help distributors and manufacturers harness the power of flowing data to drive operational excellence and profitable growth. As a result, they spend less time worrying about errors and more time on things that matter.

We are the people behind DCKAP Integrator and QA Touch.

Wait... Quick Check

Before you apply, consider if you:

- Care deeply about what you do and about making a difference in the world.
- Seek hypergrowth through consistent learning and experimenting.
- Balance work-life and life-life.
- Live a life of passion both inside and outside of work.
- Work seamlessly with multiple people– teamwork makes the dream work after all!
- Efficiently prioritize tasks and manage time.
- Move quickly, analyze and problem-solve on the go.
- Think outside the box and on your feet.
- Adapt and switch between your work and teamwork.

AI Engineers at DCKAP build production-grade AI systems and agents that solve real business challenges. They have hands-on experience designing, building, evaluating, and improving AI agents beyond proof-of-concepts and pet projects. They understand how agents, tools, skills, RAG, memory, evaluations, guardrails, and reflection work together to build reliable AI systems.

The Role

In this role, you will:

- Design and build production-grade AI agents to solve complex business and product challenges.
- Build single and multi-agent systems with reusable skills, prompts, and tools.
- Design and implement RAG and memory strategies based on different use cases.
- Build reliable tools and integrations that enable agents to interact with external systems.
- Create evaluation frameworks to measure and continuously improve agent performance.
- Implement appropriate guardrails, validations,



and fallback mechanisms.
- Use reflection, feedback, and production insights to improve agent quality and reliability.
- Collaborate with product and engineering teams to bring AI capabilities into production.

The Requirements

Experience

- 3+ years of hands-on experience building AI agents or LLM-based applications.
- Proven experience building and deploying AI systems used in production environments.
- Experience building multiple agents beyond personal projects, tutorials, or proof-of-concepts.

Expertise

- Solid understanding of AI agent architectures, tool calling, and multi-agent systems.
- Hands-on experience with different RAG approaches and memory patterns.
- Experience designing reusable skills, prompts, and tools for AI agents.
- Strong experience creating evaluations for agent performance and reliability.
- Understanding of AI guardrails, validation, and safe agent behavior.
- Experience using reflection and feedback mechanisms to improve agents.
- Strong problem-solving skills and ability to debug non-deterministic AI systems.
- Proactive mindset and strong willingness to continuously learn and experiment.

Tools + Skills

- Python.
- LangGraph, LangChain, or similar agent frameworks.
- LLM platforms such as OpenAI, Anthropic, Gemini, or AWS Bedrock.
- Vector databases, search systems, APIs, and databases.
- AWS, Azure, or GCP.

Perks you'll love

- A generous Learning & Development Budget
- Access to courses on Coursera & Stanford Continuing Studies.
- Reimbursement for relevant certifications and workshops.
- 4-Day work a week, on the second week of every month.
- Flexible working hours.
- Fully-Distributed workplace. Work remotely from anywhere in the world.
- Maternity & paternity leave.
- Travel and speak at conferences all around the world– on our tab.
- Hire-A-Star, our yummy referral program with a solid bonus.

📌 AI Engineer (India)
🏢 DCKAP
📍 India