Github MLflow Biology AutoGen Research Genomics LangChain Langgraph Pipelines Management Kubernetes Statistics TensorFlow Ethical AI Caregiving Agentic AI Code Review AI Literacy Data Science Communication Life Sciences Biotechnology Deep Learning Drug Discovery Job Evaluation Bioinformatics Responsible AI Pharmaceuticals Version Control Microsoft Azure Decision Making Experimentation Computer Vision Knowledge Graph Computer Science Causal Inference Machine Learning Containerization Model Validation Docker (Software) Business Problems Edge Intelligence Knowledge Transfer Business Priorities Amazon Web Services Predictive Modeling Feature Engineering Multi-Agent Systems Software Engineering Cloud Infrastructure Unsupervised Learning Computational Biology NumPy (Python Package) Open Source Technology Intelligent Automation Artificial Intelligence Pandas (Python Package) Stakeholder Requirements Applied Machine Learning Ribonucleic Acid Sequencing Git (Version Control System) Python (Programming Language) Ethical Standards And Conduct Medical History Documentation Scikit-Learn (Python Package) PyTorch (Machine Learning Library) MLOps (Machine Learning Operations) Application Programming Interface (API) Applications Of Artificial Intelligence Machine Learning Model Monitoring And Evaluation, Please note that this is a ‘internal job posting’ link, intended for GSK/ViiV employees only. Contingent Workers should check with the recruiter of the Job Requisition to confirm whether the job is open to contingent workers and to obtain the correct job posting hyperlink., The Applied AI team sits at the intersection of business need and technical capability within the AI/ML department. We directly support business units with AI/ML-related challenges, acting as ambassadors for responsible AI across the organization. This role is your opportunity to work at the frontier of applied machine learning in one of the world’s leading biopharma companies, translating cutting-edge AI research into real scientific and business impact

About the Role: As an Applied AI Engineer, you will be embedded within cross-functional teams to deliver practical, high-impact AI/ML solutions aligned with GSK’s R&D and business priorities. You will partner closely with scientists, product teams, and domain experts to design, build, and deploy machine learning models and AI-powered tools that accelerate drug discovery, improve decision-making, and enable responsible use of AI across the enterprise. This role is hands-on and consultative in equal measure. You will evaluate use-case feasibility, prototype solutions rapidly, architect model integrations, and transfer knowledge so that partner teams can operate independently. You will also contribute to the development of reusable patterns, baseline models, and tested pipelines for common AI/ML tasks within GSK’s approved., Advisory & Solution Design

• Provide tailored guidance to business units on AI/ML use cases, feasibility, model selection, and deployment options, particularly in scientific domains without active AI/ML engineering efforts.
• Co-design prototypes and proof-of-concepts (PoCs) with product and domain teams to validate ideas quickly and de-risk larger investments.
• Translate complex stakeholder requirements into well-scoped technical solutions with clear success criteria and handover plans.

Model Development & Deployment

• Build, train, evaluate, and iterate on ML models for real-world scientific and business problems-including but not limited to NLP/LLM applications, knowledge graphs, causal inference, computer vision, and predictive modeling.
• Package trained models into production-ready services (APIs, containerized deployments) using GSK’s cloud infrastructure (GCP/AWS/Azure).
• Develop and maintain agentic AI systems, multi-agent architectures, and LLM-based tools where appropriate.
• Share reusable patterns, baseline models, and tested pipelines for common AI/ML tasks.
• Embed privacy, ethics, and regulatory considerations into every engagement from the outset.

Knowledge Transfer & Enablement

• Run workshops, seminars, and hands-on training sessions to increase AI literacy across the organization.
• Embed within business/research units for time-limited engagements (typically 6-8 weeks) to accelerate delivery and transfer skills.
• Communicate relevant issues, requests, and opportunities from business units back to AI/ML product leads., FedRAMP Equities Fallback Terraform LangChain Claude AI Governance Salesforce AWS Lambda Opensearch Amazon Lex AI Testing Market Data RESTful API Persistence AWS Bedrock Amazon Connect Network Routing Amazon DynamoDB Ancient History LLM Application Machine Learning Cloud Development Amazon CloudWatch AWS CloudFormation Prompt Engineering Workflow Management Amazon Web Services Multi-Agent Systems Software Engineering Serverless Computing Solution Architecture Artificial Intelligence Python (Programming Language) Retrieval Augmented Generation Generative Artificial Intelligence Application Programming Interface (API) +0

Google IT Automation with Python Senior AWS Generative AI Solution Engineer Leidos

Gaithersburg, MD*Remote

JSON FedRAMP Equities Fallback Terraform LangChain Claude AI Governance Salesforce AWS Lambda Opensearch Amazon Lex AI Testing Market Data RESTful API Persistence AWS Bedrock Amazon Connect Network Routing Amazon DynamoDB Ancient History LLM Application Machine Learning Cloud Development Amazon CloudWatch AWS CloudFormation Prompt Engineering Workflow Management Amazon Web Services Multi-Agent Systems Software Engineering Serverless Computing Solution Architecture Artificial Intelligence Python (Programming Language) Retrieval Augmented Generation Generative Artificial Intelligence Application Programming Interface (API) +0

Google IT Automation with Python AI Security Risk Assessor TEKsystems

Chicago, IL*Remote

AI Agents Operations Management Governance Mitigation Cyber Risk Data Access AI Security Supply Chain Communication Risk Analysis Security Risk Cyber Security Quantification Risk Management Cyber Governance Business Valuation Financial Services Security Technology Data Loss Prevention Regulatory Compliance Full Stack Development Stakeholder Management Artificial Intelligence Large Language Modeling Business Transformation IT Security Architecture Cyber Threat Intelligence Artificial Intelligence Risk Preparing Executive Summaries Cybersecurity Risk Management Generative Artificial Intelligence

• Bachelor’s degree in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Statistics, Engineering, or a related quantitative discipline; OR equivalent professional experience as a software/ML engineer.
• 2+ years of professional experience developing and deploying machine learning models (with a Bachelor’s); 2+ years with a Master’s or PhD.
• Expertise in Python, including ML/data science libraries (PyTorch, TensorFlow, JAX, scikit-learn, pandas, numpy).
• Experience with cloud platforms (GCP, AWS, or Azure) and containerization (Docker, Kubernetes).
• Strong understanding of ML fundamentals: supervised/unsupervised learning, deep learning, model evaluation, feature engineering, and experiment tracking.
• Experience working in cross-functional teams and communicating technical concepts to non-technical stakeholders.
• Experience working in healthcare, pharma, or biological domains., * Experience in pharma, biotech, or life sciences-particularly in drug discovery, genomics, clinical data, or biological data analysis.
• Hands-on experience building LLM-based applications, agentic AI systems, RAG pipelines, or multi-agent architectures (e.g., LangChain, LangGraph, AutoGen).
• Experience with knowledge graph construction, causal inference, or large perturbation models.
• Familiarity with single-cell RNA-seq, spatial transcriptomics, CRISPR assay data, or other high-dimensional biological datasets.
• Experience with MLOps practices: CI/CD for ML, model monitoring, experiment tracking (MLflow, Weights & Biases), and reproducible research workflows.
• Contributions to open-source ML/AI projects or peer-reviewed publications in applied ML.
• Background or demonstrated interest in responsible AI, AI ethics, or model governance.
• Strong software engineering practices: version control (Git/GitHub), code review, testing, and documentation.
• Experience evaluating and integrating third-party AI/ML vendor tools and platforms.

LI-GSK

GSKAIML, Artificial Intelligence (AI), Artificial Intelligence Ethics, Artificial Neural Networks (ANNS), Classification Models, Deep Learning, Intelligent Automation (IA), Machine Learning (ML), Model Evaluation, Model Validation, Predictive Modeling, Probabilistic Modeling, Python (Programming Language), Test Documentation

• If you are based in Cambridge, MA; Waltham, MA; Rockville, MD; or San Francisco, CA, the annual base salary for new hires in this position ranges $136,125 to $226,875. The US salary ranges take into account a number of factors including work location within the US market, the candidate’s skills, experience, education level and the market rate for the role. In addition, this position offers an annual bonus and eligibility to participate in our share based long term incentive program which is dependent on the level of the role. Available benefits include health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and paid caregiver/parental and medical leave. If salary ranges are not displayed in the job posting for a specific country, the relevant compensation will be discussed during the recruitment process.

At GSK, we unite science, technology and talent to get ahead of disease together. Our ambition is to positively impact the health of 2.5 billion people over the next decade. We are building a future where state-of-the-art software, AI, and machine learning enable us to discover new therapies and personalized medicines that drive better outcomes for patients-at reduced cost and with fewer side effects.