Artificial Intelligence Engineer

Job Summary

We are looking for an AI Engineer with 23 years of experience to build and deploy scalable AI and ML solutions across cloud environments. The ideal candidate will have strong hands-on experience with Python, ML frameworks, GCP, and Vector Databases for retrieval-augmented applications. A solid conceptual understanding of algorithms and system design is essential. Experience or exposure to healthcare data and workflows is a significant advantage.

You will collaborate with cross-functional teams to develop intelligent AI systems that enhance healthcare operations, clinical decision-making, and data insights.

Key Responsibilities

1. AI/ML Model Development

• Design, train, evaluate, and optimize machine learning and deep learning models.
• Work on supervised, unsupervised, and deep learning tasks such as classification, regression, clustering, NLP, RAG systems, and predictive modeling.
• Perform feature engineering, data preprocessing, and exploratory analysis to improve model quality.

2. Building Retrieval-Augmented AI Systems

• Implement and manage Vector Databases such as

  Pinecone, Weaviate, ChromaDB, or Vertex AI Vector Search

  .
• Build and deploy

  RAG

  pipelines to enhance LLM performance using domain-specific data.
• Create embeddings workflows, document chunking strategies, and semantic search pipelines.
• Optimize retrieval speed, relevance, and indexing strategies.

3. Cloud-Based AI Engineering (GCP)

• Develop and deploy ML/AI solutions on

  Google Cloud Platform

  using:

Vertex AI (Training, AutoML, Predictions, Vector Search)

Cloud Run, Cloud Functions

Dataflow / Dataproc

4. MLOps & Deployment

• Build end-to-end ML pipelines for training, validation, deployment, and monitoring.
• Containerize models using Docker and deploy via serverless/cloud-native environments.
• Set up CI/CD workflows and automate model lifecycle management.
• Monitor model accuracy, drift, and system performance in production.

5. Data Engineering & Processing

• Work with structured, semi-structured, and unstructured datasets.
• Develop ETL/ELT pipelines for ingestion, transformation, and validation on GCP.
• Apply data governance, quality, and compliance best practices.

6. Healthcare Domain (Added Advantage)

• Work with healthcare data types such as:

EHR/EMR (HL7, FHIR)

Claims and billing datasets

Clinical notes and imaging data (DICOM)

• Understand healthcare workflows, patient journeys, terminologies, and compliance (HIPAA, PHI handling).
• Develop AI solutions for clinical, operational, or predictive-use cases.
  

7. Research & Innovation

• Stay updated with emerging trends in AI/ML, LLMs, vector search, and healthcare AI.
• Prototype and evaluate new algorithms, frameworks, and architectures.
• Identify opportunities to integrate generative AI and LLM-focused solutions into existing systems.

8. Collaboration & Communication

• Work with data scientists, cloud engineers, clinicians, and business teams.
• Translate business and clinical requirements into scalable AI solutions.
• Communicate technical concepts clearly to both technical and non-technical stakeholders.

Required Technical Skills

Programming & ML Skills

• Strong proficiency in Python and ML/DL libraries (TensorFlow, PyTorch, Scikit-Learn, Pandas, NumPy).
• Solid conceptual knowledge of ML algorithms, statistics, and model evaluation metrics.
• Practical experience in NLP and/or generative AI projects.

Cloud & Deployment Skills (GCP)

• Hands-on experience with Vertex AI, BigQuery, GCS, Cloud Run, Dataflow, etc.
• Containerization and microservices experience using Docker.

Vector Database Skills

• Experience with

  Vector DBs

  such as Milvus, Chroma, Pinecone, Weaviate, or GCP Vertex Vector Search.
• Understanding of embeddings, ANN search, semantic indexing, and RAG pipelines.
  

Data & Engineering Skills

• Strong SQL skills and data-handling expertise.
• Experience building and optimizing data pipelines.

Soft Skills

• Strong problem-solving and analytical skills.
• Excellent verbal and written communication.
• Ability to collaborate in cross-functional, fast-paced environments.
• Detail-oriented with a mindset for scalability and quality.