AI Engineer

We are looking for an In-House AI Engineer to drive our internal and product AI transformation. In this hands-on role, you will take ownership of identifying manual workflows, embedding AI solutions directly into our core web portal, and building custom tools that boost team efficiency across departments.

You will work directly with internal teams to understand operational bottlenecks and transform existing software features into intelligent, automated systems.

Key Responsibilities

• Core Portal Integration: Architect, test, and deploy AI features (e.g., intelligent search, automated tagging, contextual summaries, predictive tools) directly into our web platform.

• Internal Process Automation: Identify repetitive, time-consuming manual workflows across teams and design custom AI tools, automation scripts, or smart workflows to streamline operations.

• Solution Scoping & Prototyping: Rapidly build and evaluate Proof-of-Concepts (PoCs) using generative AI, API integrations, and open-source models to evaluate ROI before full rollout.

• API & System Integration: Connect external AI services (OpenAI, Anthropic, Google Gemini, etc.) and open-source models with existing backend infrastructure, databases, and microservices.

• Performance & Cost Management: Optimize AI model latency, API usage costs, and response accuracy to maintain high reliability and cost efficiency.

• Internal AI Support & Training: Act as the internal Subject Matter Expert (SME), guiding technical and non-technical staff on best practices for adopting AI tools safely and effectively.

Qualifications & Requirements

Education: Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related technical field (or equivalent practical experience).

Core Software & AI Skills:

Software Engineering: Strong background in full-stack or backend web development (Python, Node.js/TypeScript, React/Vue, REST APIs/GraphQL).

AI/LLM Integration: Hands-on experience integrating LLM APIs, building RAG (Retrieval-Augmented Generation) applications, and working with vector databases (Pinecone, Qdrant, Chroma, pgvector).

Automation Tools: Familiarity with workflow automation platforms (n8n, Make, LangChain, LlamaIndex) and API orchestration.

Cloud & DevOps: Experience deploying web services on AWS, GCP, or Azure using Docker.

3+ years of overall software engineering experience, with at least 1-2 years focused on integrating AI capabilities into production applications.