Cloud Computing

Cloud Computing Data Jobs

11 jobs found

Today

Senior Data Engineer - Technology

Neptune Technology Group Inc. Duluth, GA

Duluth, GA

Today

Full time Senior Amazon Web Services AWS

Apply Now

Job Description

The Senior Data Engineer role at Neptune Technology Group Inc. focuses on owning and modernizing the data pipeline architecture, migrating ETL workloads from Redshift stored procedures and legacy SSIS to scalable, maintainable pipelines built with AWS Glue and S3, and guiding the shift toward near real-time analytics using stream processing technologies such as Apache Flink and ClickHouse.

Responsibilities

Design and implement modern ETL and ELT pipelines leveraging AWS Glue, S3, and related services to replace legacy stored procedures and SSIS jobs.
Architect data transformation workflows that are testable, version-controlled, and observable.
Optimize and maintain the Redshift data warehouse, focusing on materialized views, query performance, and cost efficiency.
Advance the platform from batch ETL to near real-time stream processing by evaluating and deploying technologies such as Apache Flink, ClickHouse, Kafka, Kinesis, or equivalent.
Design pipelines capable of supporting both near real-time and batch workloads during the transition.
Collaborate with product and analytics teams to ensure data models support reporting, AI/ML initiatives, and customer-facing features.
Establish patterns and best practices for pipeline development adopted by the broader team.
Participate in production support and incident response for data infrastructure.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, with a minimum of five years in data engineering roles.
Extensive experience with AWS Glue (PySpark/Python), S3, and Redshift.
Proven track record migrating ETL workloads from legacy tools such as SSIS or stored procedures to modern cloud-native pipelines.
Strong SQL skills with best practices and SQL linting, particularly in Redshift or other columnar/MPP databases.
Experience with or strong interest in stream processing frameworks (Flink, Spark Streaming, Kafka Streams, or similar).
Familiarity with data pipeline orchestration, monitoring, and error handling patterns.
Experience with infrastructure-as-code and CI/CD for data pipelines.

Technologies

Python
PySpark
SQL
AWS Glue
S3
Redshift
SSIS
Apache Flink
ClickHouse
Kafka
Kinesis
Apache Spark Streaming
Kafka Streams
Aurora MySQL
DynamoDB
Apache Druid
dbt
Airflow
Step Functions
MQTT

Location

Onsite in either Tallassee, Alabama or Duluth, Georgia. Travel to manufacturing or customer locations may be required up to 20% of the time when necessary.

View full job page →

1d ago

AWS AI Engineer

Hudson Manpower Richmond, VA 🇺🇸 Remote

Richmond, VA

1d ago

Full time Backend Developer AI Ai Workflows

Apply Now

Job Description

This role centers on delivering hands-on AI and cloud engineering in AWS, focusing on retrieval-augmented generation systems, LLM fine-tuning, and deploying AWS native microservices to support automation and governance in production. Based in Richmond, VA with remote work available.

Responsibilities

Hands-on execution of AWS services including Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, and S3.
Implement AWS cloud services spanning infrastructure, machine learning, and AI platform services.
Develop LLM-based applications, with Retrieval-Augmented Generation using LangChain and similar frameworks.
Build cloud-native microservices, APIs, and serverless functions to enable intelligent automation and real-time data processing.
Collaborate with internal stakeholders to translate business goals into secure, scalable AI systems.
Manage the software release lifecycle, overseeing CI/CD pipelines, GitHub-based SDLC, and infrastructure as code with Terraform.
Support the development and evolution of reusable platform components for AI and ML operations.
Create and maintain technical documentation for the team and internal customers.
Demonstrate excellent verbal and written communication skills in English.

Requirements

Minimum seven years of hands-on software engineering experience with a strong emphasis on Python.
Experience with AWS services, notably Bedrock or SageMaker.
Experience fine-tuning large language models or building datasets and deploying ML models to production.
Experience with AWS Organizations and policy guardrails, including SCPs and AWS Config.
Strong background implementing RAG architectures and LangChain.
Proven IaC practices and experience building Terraform modules for AWS.
Solid experience with Git-based version control, code reviews, and DevOps workflows.
Track record of delivering production-ready software with integrated release pipelines.

Technologies

Bedrock
SageMaker
ECS
Lambda
Step Functions
DynamoDB
S3
LangChain
Transformers
PyTorch
TensorFlow
Terraform
AWS
Terraform Sentinel
Hugging Face
Node.js
Golang
Git

Benefits

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance
401(k) plan with company match and optional profit sharing
Paid vacation time
Paid bench time
Training allowance
Eligibility for referral bonuses

What you’ll get

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance
401(k) with company match and optional profit sharing
Paid vacation time
Paid bench time
Training allowance
Eligibility for referral bonuses

View full job page →

2d ago

AWS AI Engineer

Hudson Manpower Buffalo Grove, IL 🇺🇸 Remote

Buffalo Grove, IL

2d ago

Full time AI Amazon Web Services Artificial Intelligence

Apply Now

Job Description

Hudson Manpower is looking for a Senior AWS AI Engineer to design and deliver production-grade AI and ML services on AWS, with a focus on retrieval-augmented generation and fine-tuning large language models. The role supports enterprise automation and governance through AWS-native microservices, enabling scalable AI solutions across business processes. This position is based in Buffalo Grove, Illinois, with remote work options available.

Responsibilities

Direct, hands-on work with AWS services including Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, and S3.
Implement AWS cloud services spanning infrastructure, machine learning, and AI platform offerings.
Develop LLM-based applications, incorporating Retrieval-Augmented Generation (RAG) with LangChain and related frameworks.
Build cloud-native microservices, APIs, and serverless functions to enable intelligent automation and real-time data processing.
Partner with internal stakeholders to translate business goals into secure, scalable AI systems.
Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code with Terraform.
Support the development and evolution of reusable platform components for AI/ML operations.
Create and maintain technical documentation for the team and internal customers.
Communicate effectively in English, both verbally and in writing.

Requirements

7 years of hands-on software engineering experience with a strong focus on Python.
Experience with AWS services, especially Bedrock, SageMaker, ECS, and Lambda.
Familiarity with AWS Organizations and policy guardrails (SCP, AWS Config).
Experience with Retrieval-Augmented Generation (RAG) architectures and using LangChain and similar frameworks.
Proficiency in fine-tuning large language models, building datasets, and deploying ML models to production.
Strong background in Infrastructure as Code best practices with experience building Terraform modules for AWS.
Solid experience with Git-based version control, code reviews, and DevOps workflows.
Proven track record of delivering production-ready software with release pipeline integration.

Technologies

Python
AWS Bedrock, SageMaker, ECS, Lambda
AWS Organizations, SCP, AWS Config
LangChain
Transformers, PyTorch, TensorFlow
Terraform, Terraform Sentinel
AWS Step Functions, DynamoDB, S3
Hugging Face
Node.js, Golang
GitHub

Benefits

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance
401(k) with company match and optional profit sharing
Paid vacation time
Paid bench time
Training allowance
Referral bonuses

Minimum Knowledge, Skills, and Abilities Required

7 years of hands-on software engineering experience with a strong emphasis on Python.
Experience with AWS services, particularly Bedrock or SageMaker.
Familiarity with fine-tuning large language models or building datasets and deploying ML models to production.
Experience with AWS Organizations and policy guardrails (SCP, AWS Config).
Solid background in implementing RAG architectures and LangChain.
Experience with Infrastructure as Code practices and Terraform module development for AWS.
Strong Git-based version control, code reviews, and DevOps workflows.
Demonstrated success delivering production-ready software with release pipeline integration.

Nice-To-Haves

AWS or relevant cloud certifications
Policy as Code development such as Terraform Sentinel
Experience with Hugging Face, Golang, or Node.js
Exposure to FinOps and cloud cost optimization
Data science background or experience with structured and unstructured data
Awareness of data privacy and compliance best practices (PII handling, secure model deployment)

What You’ll Get

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance
401(k) with company match and optional profit sharing
Paid vacation time
Paid bench time
Training allowance
Eligibility for referral bonuses

View full job page →

2d ago

Backend/AI Engineer

Left Main REI Charlotte, NC 🇺🇸 Remote

Charlotte, NC

2d ago

$100k - $140k/yr Full time Backend Developer Ai Workflows Amazon Web Services

Apply Now

Job Description

Left Main REI is seeking a Backend/AI Engineer to design, build, and evolve backend services, data infrastructure, and GenAI-enabled features across Left Main products, with integrations to AWS, Snowflake, and Salesforce.

Responsibilities

Design, build, maintain, and optimize backend services and RESTful APIs that connect Left Main products with Salesforce, Snowflake, AWS, and external data providers.
Develop and operate serverless and cloud-native architectures using AWS services such as Lambda, API Gateway, S3, IAM, CloudWatch, EventBridge, and Kinesis.
Create robust data-processing pipelines for real estate data ingestion, enrichment, scoring, subscriptions, matching, deduplication, and delivery into customer-facing applications.
Design Snowflake-backed workflows with SQL transformations, stored procedures, Snowpark or Python processing, and performance tuning.
Architect and implement GenAI driven capabilities, including role-based agents, retrieval-augmented generation, prompt workflows, AI-assisted analysis, customer-facing copilots, and internal productivity tools.
Prototype new AI use cases, assess feasibility, and transition promising experiments into secure, reliable production services.
Collaborate with Salesforce engineers to define integration patterns using Platform Events, APIs, asynchronous processing, and precise data contracts across Salesforce, AWS, and Snowflake.
Support predictive and ML-enabled use cases such as lead scoring, buyer scoring, property intelligence, recommendation workflows, anomaly detection, and customer insights.
Enhance reliability, observability, and operational maturity across backend systems through structured logging, monitoring, alerting, automated tests, and clear incident response practices.
Contribute to CI/CD, infrastructure automation, environment management, and deployment practices for backend and data services.
Bring ideas, technical curiosity, and product-minded thinking to roadmap discussions, architecture reviews, and innovation planning.

Requirements

2+ years of professional backend engineering experience building production-grade APIs, services, data workflows, or cloud-native systems.
Strong hands-on experience with Python for backend development, scripting, automation, data processing, or AI/ML workflows.
Experience building RESTful APIs and integrating systems through APIs, webhooks, event-driven patterns, or message-based architectures.
Solid working knowledge of AWS, particularly serverless or cloud-native services such as Lambda, API Gateway, S3, IAM, CloudWatch, EventBridge, SQS, SNS, Kinesis, or similar.
Strong SQL skills and experience with modern data warehouses, preferably Snowflake.
Proven ability to design reliable, secure, observable, cost-conscious, and maintainable systems.
Familiarity with modern AI and GenAI development patterns, including LLM APIs, prompt engineering, RAG, embeddings, vector databases, model evaluation, agent workflows, or AI-assisted automation.
Ability to use contemporary AI development tools such as ChatGPT, Claude, Cursor, or GitHub Copilot to improve development speed, code quality, and documentation.
Experience with CI/CD pipelines, GitHub Actions, automated testing, infrastructure as code, or deployment automation.
Comfort working in a fast-moving startup environment where priorities evolve and engineers are expected to own outcomes beyond individual tickets.
Strong communication and collaboration skills to partner across product, engineering, data, Salesforce, and leadership teams.

Technologies

Python
AWS
Lambda
API Gateway
S3
IAM
CloudWatch
EventBridge
Kinesis
SQS
SNS
Snowflake
Snowpark
SQL
Salesforce
Salesforce Platform Events
GitHub Actions
GitHub Copilot
Cursor
ChatGPT
Claude
LLM APIs
prompt engineering
RAG
embeddings
vector databases

Benefits

401(k)
401(k) matching
Dental insurance
Flexible remote work arrangements
Life insurance
Paid time off
Vision insurance
Health insurance
Opportunities for professional growth and development
Collaborative and inclusive work culture
Salesforce certification incentives
Flexible schedule
Professional development assistance

Compensation

Salary range: USD 100,000 to 140,000 per year
Pay range: $100,000 - $140,000 per year
Generous benefits package
Paid holidays

Location and Work Arrangement

Location: Charlotte, NC, with remote work options

Work arrangement: Remote

Job type: Full-time

Licenses and Certifications

Salesforce Certification (Required)

View full job page →

3d ago

AWS AI Engineer

Hudson Manpower Pittsburgh, PA 🇺🇸 Remote

Pittsburgh, PA

3d ago

Full time AI Amazon Web Services Artificial Intelligence

Apply Now

Job Description

Hudson Manpower is seeking a Senior AWS AI Engineer to architect and operate production AI/ML applications on AWS, with a focus on retrieval-augmented generation, fine-tuning large language models, and cloud-native microservices. The role centers on delivering secure AI services that integrate with live infrastructure data, built to scale and operate within live environments. The position is based in Pittsburgh, PA with remote work options and requires seven years of hands-on software engineering experience.

Responsibilities

Hands-on work with AWS services including Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, and S3 to implement cloud infrastructure, machine learning features, and AI platform capabilities.
Own the deployment of AWS cloud services, spanning infrastructure, ML workflows, and AI platform services.
Develop and maintain LLM based applications, including Retrieval-Augmented Generation (RAG) using LangChain and related frameworks.
Build cloud-native microservices, APIs, and serverless functions to enable intelligent automation and real-time data processing.
Collaborate with internal stakeholders to translate business goals into secure, scalable AI systems.
Manage the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code with Terraform.
Support a reusable set of platform components for AI/ML operations and their evolution.
Create and maintain technical documentation for the team and internal customers.
Provide clear verbal and written communication in English.

Requirements

Seven years of hands-on software engineering experience with a strong emphasis on Python.
Experience with AWS services, particularly Bedrock or SageMaker.
Familiarity with fine-tuning large language models or building datasets and deploying ML models to production.
Experience navigating AWS organizations and policy guardrails such as SCP and AWS Config.
Solid track record implementing RAG architectures and using LangChain.
Proven experience with Infrastructure as Code best practices and building Terraform modules for AWS.
Strong background in Git-based version control, code reviews, and DevOps workflows.
Proven success delivering production-ready software with release pipeline integration.

Technologies

Python, Bedrock, SageMaker
ECS, Lambda, Step Functions
DynamoDB, S3
LangChain, Transformers, PyTorch, TensorFlow
Terraform, Git, GitHub
Hugging Face, Node.js, Golang
Terraform Sentinel, AWS Config, SCP

Benefits

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance
401(k) with company match and optional profit sharing
Paid vacation time
Paid bench time
Training allowance
Referral bonuses

Top Skills

Experience with AWS services including Bedrock, SageMaker, ECS, and Lambda
Strong knowledge of AWS organizations and policy guardrails (SCP, AWS Config)
Proficiency in implementing RAG architectures and using ML tooling such as Transformers, PyTorch, TensorFlow, and LangChain
Infrastructure as Code best practices and building Terraform modules for AWS
Fine-tuning large language models, building datasets, and deploying ML models to production
Git-based version control, code reviews, and DevOps workflows
AWS or relevant cloud certifications
Data privacy and compliance best practices, including secure model deployment
Data science background or experience with structured and unstructured data
Exposure to FinOps and cloud cost optimization
Hugging Face, Node.js
Policy as Code development (Terraform Sentinel)

View full job page →

3d ago

AWS AI Engineer

Hudson Manpower Scottsdale, AZ 🇺🇸 Remote

Scottsdale, AZ

3d ago

Full time AI Amazon Web Services Artificial Intelligence

Apply Now

Job Description

Senior AI AWS Engineer focused on building AI and ML applications on AWS, including retrieval-augmented generation systems, fine-tuning LLMs, and AWS-native microservices in a remote US setting.

Responsibilities

Engage hands-on with AWS services such as Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, and S3.
Lead the implementation of AWS cloud services across infrastructure, machine learning, and AI platform layers.
Develop LLM based applications, including Retrieval-Augmented Generation (RAG) using LangChain and related frameworks.
Build cloud-native microservices, APIs, and serverless functions to enable intelligent automation and real-time data processing.
Partner with internal stakeholders to translate business goals into secure, scalable AI systems.
Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code with Terraform.
Support development and evolution of reusable platform components for AI/ML operations.
Create and maintain technical documentation for the team and internal customers.
Communicate effectively in English, both verbally and in writing.

Requirements

7 years of hands-on software engineering experience with a strong emphasis on Python.
Experience with AWS services, especially Bedrock or SageMaker.
Familiarity with fine-tuning large language models or building datasets and deploying ML models to production.
Proven experience with AWS organizations and policy guardrails such as SCPs and AWS Config.
Strong track record implementing RAG architectures and using LangChain.
Solid experience in Infrastructure as Code practices and building Terraform modules for AWS cloud.
Proficiency with Git-based version control, code reviews, and DevOps workflows.
Proven success delivering production-ready software with release pipeline integration.

Technologies

Python, Bedrock, SageMaker
Lambda, Step Functions, DynamoDB, S3, ECS
LangChain, Transformers, PyTorch, TensorFlow
Terraform, Terraform Sentinel
Hugging Face, Node.js, Golang
Git, GitHub

Benefits

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance provided
401(k) with a company match and optional profit sharing
Paid vacation time
Paid Bench time
Training allowance
Eligible for referral bonuses

Top Skills

Must Have AWS services Bedrock, SageMaker, ECS, and Lambda
Experience with AWS organizations and policy guardrails (SCP, AWS Config)
Experience implementing RAG architectures with LangChain and ML tooling (Transformers, PyTorch, TensorFlow)
Infrastructure as Code best practices and Terraform module development for AWS
Fine-tuning LLMs, building datasets, and deploying ML models to production
Git-based version control, code reviews, and DevOps workflows
Nice to have AWS or relevant cloud certifications
Nice to have data privacy and compliance practices (PII handling, secure model deployment)
Nice to have data science background or experience with structured/unstructured data
Nice to have exposure to FinOps and cloud cost optimization
Nice to have Hugging Face, Node.js
Nice to have Policy as Code development (Terraform Sentinel)

Supervisory Responsibilities

None

Minimum Knowledge, Skills, and Abilities Required

7 years of hands-on software engineering experience with a strong emphasis on Python.
Experience with AWS services, especially Bedrock or SageMaker.
Familiarity with fine-tuning large language models or building datasets and deploying ML models to production.
Proven experience with AWS organizations and policy guardrails (SCP, AWS Config).
Strong track record implementing RAG architectures and using LangChain.
Solid experience in Infrastructure as Code practices and building Terraform modules for AWS cloud.
Proficiency with Git-based version control, code reviews, and DevOps workflows.
Proven success delivering production-ready software with release pipeline integration.

What You'll Get

Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance provided
401(k) with company match and optional profit sharing
Paid vacation time
Paid Bench time
Training allowance
Referral bonuses eligibility

View full job page →

5d ago

Bioinformatics Data Scientist

Spectrix Analytical Services, LLC Cambridge, MA

Cambridge, MA

5d ago

$60+/hr Contract Python Amazon Web Services Analytics

Apply Now

Job Description

Contribute to drug discovery and translational research by enabling computational proteomics, multi-omics analytics, scalable data workflows, and AI driven scientific analyses.

Responsibilities

Examine large scale proteomics and multi-omics data to support biomarker discovery, pharmacodynamic analysis, pathway and causal inference, disease biology, and drug discovery programs.
Design scalable, reproducible, cloud‑ready analytical workflows, data pipelines, reports, dashboards, APIs, and data products for scientific users.
Utilize statistical modeling, machine learning, and AI/LLM powered approaches to enhance biological interpretation, knowledge extraction, workflow automation, and scientific decision support.
Merge proteomics data with orthogonal modalities such as transcriptomics, genomics, genetics, perturbation data, metadata, and translational annotations.
Partner with computational scientists, mass spectrometry specialists, discovery biologists, translational researchers, and data science teams to define analytical strategies and clearly communicate results.
Champion data QC, reproducible analysis, workflow development, software engineering standards, and responsible use of AI‑assisted scientific tools.

Requirements

Doctorate in bioinformatics, computational biology, data science, statistics, computer science, systems biology, biology, biochemistry, chemistry, engineering, or a related quantitative field.
Hands-on experience with high‑dimensional biological or biomedical data such as proteomics, LC‑MS, Olink, SomaScan, transcriptomics, single‑cell data, spatial omics, genomics, genetics, or other omics modalities.
Strong proficiency in R and/or Python for data analysis, statistical modeling, visualization, and reproducible scientific computing.
Solid understanding of experimental design, data QC, normalization, missing-value assessment and imputation, feature selection, and advanced statistical modeling for high‑dimensional data, including linear and mixed‑effects models, Bayesian methods, biological signal deconvolution, pathway interpretation, and communicating findings.
Experience with scalable computing and cloud environments, including AWS services and workflow‑based analysis systems for large scientific datasets.
Proficiency with machine learning, AI, and LLM powered analytics, including developing robust agentic or multi‑agent frameworks for scientific workflows.
Familiarity with drug discovery, translational research, biomarker discovery, perturbation biology, pharmacodynamic studies, disease biology, or related biomedical contexts.
Ability to work independently on complex analytical problems and convey results clearly to scientific stakeholders.

Technologies

R
Python
AWS
Nextflow
Snakemake
Airflow
Docker
AWS Batch
ECS
DIA-NN
Spectronaut
MaxQuant
FragPipe
Proteome Discoverer
Skyline
Shiny
Dash
Streamlit
FastAPI
Flask
Posit/RStudio
Posit Connect
Claude
Parquet
SQL
Git
Olink
SomaScan

Benefits

401(k)
401(k) matching
Dental insurance
Employee assistance program
Flexible schedule
Flexible spending account
Health insurance
Health savings account
Life insurance
Paid time off
Parental leave
Professional development assistance
Referral program
Retirement plan
Tution reimbursement
Vision insurance

Preferred Qualifications

Postdoctoral, industry, or equivalent applied research experience after PhD.
Direct experience with computational proteomics across platforms such as LC‑MS proteomics, phosphoproteomics, DIA/SWATH, DDA, TMT, label‑free quantification, PTM analysis, spectral library generation and prediction, and affinity‑based proteomics such as Olink or SomaScan.
Hands‑on experience with proteomics software, outputs, or data structures from tools like DIA‑NN, Spectronaut, MaxQuant, FragPipe, Proteome Discoverer, Skyline, or similar platforms.
Experience building reusable scientific workflows, analytical pipelines, applications, dashboards, APIs, reports, or self‑service data products for scientists.
Experience deploying analytical workflows or data products on AWS or comparable clouds using Nextflow, Snakemake, Airflow, Docker, AWS Batch, ECS, or equivalent systems.
Experience delivering R and Python software and data products with Posit/RStudio, Posit Connect, Shiny, Dash, Streamlit, FastAPI, Flask, or similar platforms.
Strong software engineering practices including Git, modular code, documentation, testing, and reproducible workflow development.
Hands‑on experience building LLM enabled applications or workflows using Claude or other large language models, including RAG systems, tool‑using agents, prompt engineering, evaluation frameworks, LLMOps, or scientific knowledge extraction.
Experience applying ML or foundation-model approaches to biological data, including representation learning, multimodal modeling, classification/regression, embedding‑based retrieval, generative AI, or related methods.
Experience with data modeling, SQL, Parquet, metadata management, data lake architectures, or large scale biological data warehouses.
Strong publication record, open source contributions, or demonstrated delivery of reusable computational tools, analytical platforms, scientific workflows, or production quality internal data products.

Strong Differentiators

Ability to bridge computational proteomics, biological interpretation, cloud engineering, bioinformatics methodology development, and AI/ML/LLM workflow implementation for life science applications.
Proven success building tools, analytical methods, or platforms adopted by experimental, translational, or computational scientists.
Contributions to peer reviewed publications in bioinformatics, computational biology, proteomics, machine learning, systems biology, or related fields.
Experience designing AI assisted, machine learning, or agentic workflows that are reproducible, traceable, scientifically reliable, and suitable for biological and biomedical research.
Strong understanding of connecting omics data, pathway biology, perturbation data, genetics, and drug discovery questions into reusable analytical systems.
Ability to develop and publish novel analytical methodologies when appropriate.
Track record applying AI and ML techniques to life science datasets, including biomarker discovery, target identification, predictive modeling, knowledge extraction, or multi‑omics integration.
Ability to help shape future scientific AI strategy beyond executing predefined analyses.