Engineer

 

Description:

Crowdception Inc. is seeking a Senior ML Ops Engineer / Lead with deep expertise in Computer Vision, Machine Learning, Large Language Models (LLMs), RAG architectures, and Agentic AI systems.

This role is ideal for an engineer/tech lead who can bridge MLOps, product engineering, and applied AI

research, helping build next-generation healthcare technologies including multimodal models,

AR/XR-enabled tools, real-time inference systems, and clinical AI assistants.

Your work will directly influence patient care, diagnostics, documentation workflows, and clinical

efficiency, while ensuring accuracy, reliability, and high-quality engineering in all deployed products.

Key Responsibilities

LLMs, GenAI & Agentic AI

● Build, fine-tune, and deploy LLMs (OpenAI, Anthropic, Llama, Mistral, DeepSeek) for clinical

documentation, summarization, and decision support.

● Architect and implement RAG systems using LangChain, LlamaIndex, and vector databases.

● Develop agentic AI systems with tool-calling, planning, and autonomous workflow capabilities.

● Optimize multimodal AI systems combining text, vision, voice, and structured clinical data.

 

Computer Vision & Multimodal AI

● Design and optimize CV models (object detection, OCR, segmentation, video analytics).

● Deploy edge and on-device inference using TensorRT, TFLite, ONNX, and PyTorch Mobile.

● Integrate CV pipelines into AR/XR applications and real-time healthcare environments.

 

MLOps & Production Deployment

● Build and manage ML pipelines using MLflow, Kubeflow, SageMaker, Airflow, Prefect, or Ray.

● Deploy scalable systems using Docker, Kubernetes, Helm, and cloud-native services.

● Implement model versioning, experiment tracking, and automated training workflows.

● Monitor model performance using Prometheus, Grafana, and OpenTelemetry.

● Manage feature stores (Feast, Tecton) and dataset versioning with DVC.

 

AR/XR, Voice AI & Interactive Systems

● Integrate AR/XR frameworks (Unity XR, OpenXR, Snapdragon Spaces) into healthcare use

cases.

● Build voice-enabled interfaces using Whisper, DeepSpeech, or major cloud speech APIs.

● Develop interactive systems that support hands-free clinical workflows.

 

Healthcare Integration

● Integrate products with EHR/EMR ecosystems using FHIR, HL7, SMART on FHIR, OAuth2,

HL7 CDS Hooks.

● Build robust data exchange layers tailored for clinical workflows and healthcare operations.

● Ensure data-handling practices align with healthcare compliance standards (HIPAA, GDPR,

PDPL) from a process—not cybersecurity—perspective.

 

Required Qualifications (Mandatory)

● Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Engineering, or

related fields.

● 5+ years of experience in ML Ops, Machine Learning Engineering, Computer Vision, or AI

system deployment.

● Proven experience deploying LLMs, RAG systems, and agentic AI frameworks into production.

● Strong proficiency in Python and ML/AI frameworks such as PyTorch, TensorFlow, HuggingFace

Transformers.

● Hands-on experience with Docker, Kubernetes, and cloud compute environments

(AWS/GCP/Azure).

● Experience with vector databases (Pinecone, Weaviate, FAISS, Milvus).

● Familiarity with healthcare data standards (FHIR, HL7) and clinical data integration.

● Strong understanding of healthcare data privacy standards (HIPAA, GDPR, PDPL) from a

workflow and compliance standpoint.

● Excellent communication and cross-functional collaboration skills.