AI Engineer

Overview:

We are looking for an AI Engineer (AI Engineer – Conversational AI & Application Development).

The candidate will need to be collaborative, organised, think out-of-the-box, and be ready to pursue new opportunities. Most importantly, this role is for an individual who is passionate about making a difference through healthcare.

Role Summary

We are seeking a talented AI Engineer to design, develop, and deploy sophisticated conversational agents and AI‑powered applications. Successful candidate will combine strong software‑engineering skills with a deep interest in modern AI techniques. This role will give you the opportunity to work with the latest multi‑agent orchestration frameworks—LangGraph, AutoGen, CrewAI, and Google's Agent Developer Kit (ADK)—while harnessing the scalability of Google Cloud Platform (GCP) services such as BigQuery, Cloud Run, Kubernetes (GKE), and IAM.

Key Responsibilities

- Design and build multi‑agent conversational systems leveraging LangGraph, AutoGen, CrewAI, ADK, or similar frameworks.

- Collaborate with product managers, designers, and engineers to define user journeys, agent capabilities, and integration points.

- Develop robust, scalable backend services and APIs (Python/Golang/Node.js/Java) to power AI agents and orchestration workflows.

- Apply advanced Natural Language Processing (NLP), Retrieval‑Augmented Generation (RAG), and LLM fine‑tuning techniques for intent recognition, entity extraction, and dynamic dialogue management.

- Deploy and operate conversational workloads on GCP Cloud Run (serverless) and Kubernetes (GKE), following DevOps best practices and implementing secure access controls with IAM.

- Leverage BigQuery for large‑scale data analytics to train, evaluate, and refine ML models and conversational metrics.

- Design and develop interactive dashboards in Power BI or Tableau to visualize conversational KPIs, model performance, and business insights.

- Integrate conversational agents with enterprise systems (databases, CRMs, ERPs) and third‑party APIs.

- Implement observability, evaluation, and monitoring for agent performance (latency, accuracy, hallucination rate) using tools like Arize Phoenix, Langfuse, and LangSmith.

- Contribute to the full software development lifecycle: requirements, architecture, coding, testing, CI/CD, deployment, and maintenance.

- Troubleshoot and resolve production issues to ensure high availability and optimal performance.

- Document technical designs, decisions, and model specifications for cross‑functional reference.

Required Qualifications

- Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related field.

- Proficiency in one or more backend languages/frameworks:

- Python (FastAPI, Flask, Django)

- Java (Spring Boot)

- Node.js (Express, NestJS)

- Go (Gin, Fiber)

- Experience building RESTful or GraphQL APIs and designing microservices.

- Solid grasp of machine‑learning fundamentals: supervised/unsupervised learning, evaluation metrics, feature engineering.

- Familiarity with modern NLP libraries (spaCy, Haystack, Hugging Face Transformers, LlamaIndex).

- Strong version‑control habits (Git, trunk‑based development) and CI/CD pipelines.

- Excellent problem‑solving, communication, and collaboration skills.

- Passion for continuous learning and adapting to emerging AI technologies.

Preferred Qualifications

- Hands‑on experience creating and deploying production‑grade AI chat agents or virtual assistants.

- Working knowledge of LangGraph, AutoGen, CrewAI, ADK, or comparable agent‑orchestration / workflow frameworks.

- Experience with Large Language Models (Gemini, GPT‑4, Claude, Mistral, etc.) and vector databases (Weaviate, Chroma, Pinecone).

- Deep experience with Google Cloud Platform (GCP):

- BigQuery for analytical workloads and feature stores.

- Cloud Run (serverless) for scalable, cost‑efficient microservices.

- Google Kubernetes Engine (GKE) for container orchestration and workload portability.

- Identity and Access Management (IAM) for secure, least‑privilege access control.

- Business‑intelligence expertise in building dashboards and reports with Power BI or Tableau.

- Familiarity with other cloud providers (AWS, Azure) and their AI/ML services.

- Understanding of MLOps (model registry, continuous training, drift monitoring) and LLM observability tools such as Arize Phoenix, Langfuse, and LangSmith.

- Database skills in both SQL (PostgreSQL, BigQuery) and NoSQL (Firestore, DynamoDB).

- Contributions to open‑source AI/ML projects or technical blogging/presentations.

---