GKIM Digital ★ 1.0

About the Role:

As a Mid-Level AI Engineer, you will play a key role in designing, building, and deploying intelligent systems to address real-world challenges in the healthcare industry. You’ll collaborate with cross-functional teams to transform AI research into scalable production solutions, with a focus on automation workflows, conversational agents, and large language model integration.

Your expertise in cloud infrastructure (AWS/GCP), MLOps, and intelligent agents will help shape next-generation AI-driven healthcare applications.

Responsibilities:

1.     AI Model Development & Deployment:

·  Design, train, evaluate, and fine-tune machine learning/deep learning models (e.g., NLP, computer vision, tabular ML) using TensorFlow, PyTorch, scikit-learn, or similar.

·  Implement retrieval-augmented generation (RAG), Sparse Fine-Tuning (SFT), and LoRA for LLMs like GPT-4, Gemini, DeepSeek R1, Grok3, etc.

·  Deploy models using AWS SageMaker, Vertex AI, Docker, and Kubernetes, ensuring performance and scalability.

2.     Automation Flow & Agent Design:

·  Design and build AI-powered workflows using n8n, LangChain, or custom agent frameworks.

·  Architect multi-agent collaboration patterns, intelligent routing, and task orchestration.

·  Integrate conversational agents with business logic via API gateways, event-driven architectures, or MCP (Multi-Channel Processing) servers.

3.     Data Engineering & Analysis:

·   Preprocess and analyze structured and unstructured data using SQL, Python, Pandas, Spark, etc.

·   Develop robust ETL pipelines for training and inference using AWS Glue, Airflow, or Cloud Functions.

·   Manage data versioning and lineage using Data Catalogs or Lake Formation.

4.     Collaboration & Integration:

·   Partner with data scientists, engineers, and product stakeholders to align AI initiatives with business objectives.

·   Integrate AI models into microservices, RESTful APIs, or serverless functions.

5.     Optimization & Innovation:

·   Continuously improve model performance through hyperparameter tuning, model distillation, and quantization.

·   Explore and prototype cutting-edge technologies in ethical AI, explainable AI (XAI), and autonomous agents.

6.     Documentation & Compliance:

·   Maintain clear, structured documentation for models, workflows, and APIs.

·   Ensure compliance with HIPAA, GDPR, and other data governance standards.

Required Skills & Qualifications

1.     Technical Skills:

·    Proficiency in Python (must-have); familiarity with Java or C++ is a plus.

·    Strong understanding of neural networks, transformers, LLMs, NLP, and computer vision.

·    Hands-on experience with AWS (Lambda, SageMaker, EC2, S3, DynamoDB, Step  Functions) or GCP (Vertex AI, BigQuery, Cloud Functions, Pub/Sub).

·    Experience with MLOps tools such as MLflow, Kubeflow, KServe, Weights & Biases, or Triton Inference Server.

·    Familiarity with CI/CD pipelines for ML (GitHub Actions, GitLab, CodePipeline).

·    Knowledge of automation tools (e.g., n8n, Zapier, Airflow).

·    Experience designing and deploying LLM agents, task-oriented bots, or MCP server-based architectures.

2.     Soft Skills:

·    Analytical thinking and a proactive problem-solving mindset.

·    Strong communication skills, especially in translating complex concepts into actionable insights.

·    Adaptability in a fast-paced and innovation-driven environment.

·    Fluent in speaking and written English

3.     Education:

·    Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related fields.

·    Relevant certifications (e.g., TensorFlow Developer, AWS Certified ML Specialist) are a strong plus.

4.     Experience:

·   2–3 years of experience in AI/ML engineering, ideally in a production environment.

·   Hands-on experience with LLMs, agent-based design, RAG, and workflow orchestration.

·    Healthcare AI experience is a plus (e.g., medical NLP, diagnostic AI, patient triage automation).