We are seeking a highly motivated and skilled Machine Learning Engineer to join our team. In this role, you will focus on building AI and automation solutions for clients in healthcare and life sciences sectors, working on back-end development, data transformation, experimenting with AI/ML models, and selecting the most suitable solution to address our client's pain points. You will play a critical role in designing scalable and secure solutions within an AWS/ Azure based infrastructure. The ideal candidate has experience in AI/ML, a strong background in software engineering, and a passion for staying up-to-date with the latest advancements in AI and machine learning, including a willingness to explore and experiment with cutting-edge technologies to tackle new challenges.

Requirements

Key Responsibilities

Design and implement systems to automate operational workflows, including data ingestion, transformation, and integration with client systems.
Research, design and experiment with AI models for tasks such as natural language processing (NLP), OCR-based document validation, and signature detection.
Model Development: Design, develop, and train machine learning models and algorithms using appropriate techniques and frameworks
Evaluation and Optimization: Evaluate the performance of machine learning models and optimize them for better accuracy, reliability, and efficiency
Feature Engineering: Extract and engineer relevant features from lifesciences/healthcare data to enhance model performance and predictive power
Collaborate with cross-functional teams to ensure alignment with client requirements and compliance standards.
Build scalable solutions within the AWS/ Azure ecosystem
Optimize workflows for security, efficiency, and compliance with lifesciences/ healthcare and privacy regulations (e.g., GxP, HIPAA).
Explore and prototype RPA solutions to automate repetitive tasks in non-API-accessible systems (nice-to-have)

Required Qualifications

Bachelor's degree in Computer Science, Engineering, or a related field. Master's degree or higher is a plus
Data Processing: Proficient in designing and implementing ETL pipelines, data transformation, and integration.
Machine Learning: Ability to explore, test, and implement a wide range of machine learning models for diverse problem domains, particularly in OCR, NLP, or document analysis.
AWS/ Azure Expertise: Hands-on experience with AWS services like EC2, Lambda, S3, RDS, VPC etc OR Azure services like Azure Functions, Azure Data Factory, and Azure AI.
Compliance Knowledge: Familiarity with handling sensitive data and meeting regulatory requirements (e.g., HIPAA).
Collaboration: Strong communication skills and experience working in cross-functional teams.
Results: Proven ability to lead projects and deliver results within tight timelines.

Preferred Qualifications

RPA: Experience with tools like Microsoft Power Automate, UiPath, or Automation Anywhere.
LifeSciences or Healthcare Industry: Background in Clinical trials, drug discovery or healthcare, compliance workflows, or similar domains.

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Machine Learning Engineer