Artificial Intelligence Engineer

Responsibilities:

1. Generative AI Feature Development:

   Work on features powered by

   LLMs (Large Language Models)

   such as chatbots, assistants, content generators, and automation workflows.

2. Agentic Flow Creation:

   Help design and implement

   agentic workflows

   – multi-step AI flows where models call tools/APIs, access data sources, and execute actions based on user input.

3. Prompt & Workflow Design:

   Design, test, and refine prompts, system instructions, and control flows to make AI features more reliable, safe, and user-friendly.

4. Backend Data Handling:

   Implement backend logic to

   store, transform, and serve data

   for AI features, working closely with backend engineers.

5. SQL & Storage Integration:

   Work with

   SQL databases

   (e.g., PostgreSQL/MySQL) to store chat histories, logs, user data, configuration, and AI-related metadata.

6. Integration with APIs & Services:

   Integrate AI services (e.g., LLM providers, embedding APIs, vector stores) into existing backend services and client applications.

7. Experimentation & Evaluation:

   Run small experiments comparing prompts, parameters, and workflows; document results and suggest improvements.

8. Collaboration with Product & Engineering:

   Work with product managers, backend/frontend teams, and QA to refine requirements and deliver AI features that solve real user problems.

9. Documentation:

   Maintain clear documentation for AI workflows, prompts, data flows, and integration points to help other engineers understand and extend your work.

10. Continuous Learning:

    Stay updated with

    Generative AI tools, frameworks, and best practices

    , and share learnings with the team.

Requirements:

Education:

Bachelor’s degree in Computer Science, Information Technology, Data/AI-related field, or equivalent practical skills through projects.

Generative AI Basics (Must Have):

i. Good understanding of Generative AI concepts: LLMs, prompts, temperature, tokens, context window, etc.

ii. Hands-on exposure to at least one LLM provider (OpenAI / Claude / Gemini / etc.) via APIs or SDKs.

Agentic Flow Experience (Must Have – Project Level):

i. Some experience building or configuring agentic flows:

ii. Multi-step workflows where models call tools/APIs

iii. Using frameworks like LangChain / LlamaIndex / similar OR self-built orchestration.

iv. Comfort thinking in terms of “tool-using AI agents” rather than just single-prompt responses.

Backend & Data Handling (Must Have):

i. Basic experience with backend development in any language (e.g., Node.js, Python, etc.).

ii. Ability to build simple APIs or services to connect AI flows with other parts of the system.

iii. Solid understanding of SQL and experience writing basic queries (CRUD, simple joins, filters).

Programming Skills:

i. Good coding skills in at least one general-purpose language (Python or JavaScript/TypeScript preferred).

ii. Familiarity with Git and standard development workflows.

ML/Traditional AI (Optional but Good to Have)

i. Basic understanding of machine learning fundamentals (train/val/test, common metrics)

ii. Any project experience with scikit-learn / PyTorch / TensorFlow is a plus, but not mandatory.

Problem-Solving & Ownership:

i. Strong logical reasoning, debugging skills, and willingness to work through ambiguity.

ii. Proactive, self-driven, and comfortable taking ownership of small features or experiments.

Communication & Collaboration:

i. Ability to clearly explain ideas and trade-offs to both technical and non-technical stakeholders.

ii. Comfortable working in a fast-paced, client-focused environment with changing priorities.

Projects/Portfolio (Preferred):

i. 1–3 Generative AI / LLM / Agentic projects:

ii. Example: chatbots, RAG-based Q&A, workflow/agent systems, AI assistants, internal tools using LLMs.

iii. GitHub/portfolio links or live demos that you can walk us through.