Applied AI Engineer, Enterprise GenAI Job at Scale (San Francisco)

Director, Applied AI Engineering- Enterprise

Join the team shaping the future of AI at Scale. The Applied AI team works on th...

Location

United States , San Francisco; New York

Salary:

285200.00 - 356500.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

8+ years of experience building, deploying, and scaling ML systems in production, including Enterprise-grade Gen AI and agentic applications
6+ years of people leadership experience, including managing managers and leading multi-team organizations delivering customer-facing AI products
Deep applied expertise in Gen AI, including LLM-based systems, agent orchestration, evaluation and reliability frameworks, and production system design
Experience owning end-to-end AI product delivery—from problem definition and technical architecture to deployment, iteration, and scaling
Strong technical judgment across software engineering, system design, and applied ML fundamentals
Exceptional communication and stakeholder management skills, with the ability to influence executives, customers, and cross-functional partners
Bachelor’s degree required
Master’s or PhD in Computer Science or a related field strongly preferred

Job Responsibility

Drive enterprise impact with GenAI: Partner directly with customers to identify high-leverage opportunities for generative AI, design solutions, and deliver production systems that create clear business value
Lead and scale a GenAI-native Applied AI organization: Build, mentor, and manage multiple teams of Applied AI Engineers and ML Engineers who are native to modern GenAI technologies, agentic systems, and multimodal models
Set technical and strategic direction: Define best practices for agent development, evaluation, deployment, and iteration across customer engagements and internal product efforts
Bridge frontier research to real-world applications: Translate advances in LLMs, multimodal models, and agentic workflows into practical, scalable solutions across industries
Discover model limitations and inspire innovation: Identify failure modes, inefficiencies, and gaps in current models
use these insights to influence internal research, product roadmaps, and customer strategy
Partner cross-functionally: Work closely with Product, Research, Sales, and Leadership to shape Applied AI offerings, prioritize investments, and guide long-term platform evolution
Influence the broader AI ecosystem: Represent Scale’s Applied AI capabilities with customers and partners, and help position Scale as a trusted leader in enterprise GenAI deployment

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
equity based compensation
commuter stipend

Fulltime

Distinguished Engineer – AI Security

We're building a world of health around every individual — shaping a more connec...

Location

United States , Scottsdale

Salary:

175100.00 - 334750.00 USD / Year

CVS Health

Expiration Date

June 30, 2026

Requirements

15+ years of AI experience, including significant depth in advanced technical or architectural roles
5+ years of cybersecurity experience defining and integrating security standards and controls that aligned to established frameworks such as NIST CSF
Deep expertise in AI security concepts such as adversarial ML, secure model deployment, AI agent authorization, AI data loss protection, AI safety, and AI risk management
Strong background in Zero Trust architecture and hybrid infrastructure security
Demonstrated ability to lead and influence large-scale, cross-functional security initiatives
Hands-on experience building, deploying, and securing AI systems and platforms in enterprise environments
Practical experience applying AI security and risk management frameworks in real-world engineering contexts
AI Security Frameworks: MITRE ATLAS, NIST RMF, ISACA AI Audit Toolkit, and emerging ISO/IEC AI security standards
AI Technologies: Expert conceptual and hands-on implementation knowledge of core ML and generative AI technologies including transformer-based NLP, LLM-based generative AI and agentic AI
AI Risk Management & Model Security: Threat modeling, adversarial defenses, model lifecycle security, and vulnerability management

Job Responsibility

Define and help execute the enterprise AI security strategy, spanning secure model selection, development, and deployment criteria, adversarial threat mitigation, and alignment with emerging AI governance requirements
Design, build, and maintain reusable AI security frameworks, reference patterns, and technical standards for model integrity, secure data pipelines, and privacy-preserving machine learning
Perform hands-on security assessments of AI systems, identify risks, and provide mitigation guidance based on AI security posture management and detection findings
Drive innovation in AI security techniques, controls, and tooling through applied research and practical implementation
Apply and guide the application of AI security frameworks such as MITRE ATLAS, NIST RMF, and emerging ISO/IEC AI standards to secure the end-to-end AI lifecycle
Apply Zero Trust principles to hybrid and cloud infrastructure environments supporting AI workloads, including workload identity, segmentation, and continuous verification
Partner closely with Enterprise Architecture and Platform Engineering to integrate AI security controls into infrastructure design patterns and shared services
Guide and, where appropriate, directly implement security capabilities across on-premises and cloud platforms to ensure consistent protection for AI and traditional systems
Hands-on Engineering & Prototyping: Design and build proof-of-concept solutions, reference implementations, and reusable components to validate AI security and infrastructure security approaches
Framework and Pattern Development: Architect repeatable security patterns and guardrails that can be adopted by data science, engineering, and platform teams

What we offer

Affordable medical plan options
401(k) plan (including matching company contributions)
Employee stock purchase plan
No-cost programs for all colleagues including wellness screenings, tobacco cessation and weight management programs, confidential counseling and financial coaching
Paid time off
Flexible work schedules
Family leave
Dependent care resources
Colleague assistance programs
Tuition assistance

Fulltime

GenAI Engineering Lead

We are seeking an exceptional and highly motivated GenAI Tech Lead to join our d...

Location

India , Pune

Salary:

Not provided

Citi

Expiration Date

Until further notice

Requirements

10-15 years of relevant experience in Apps Development or systems analysis role, with a significant portion dedicated to AI/ML and demonstrable hands-on experience in designing, building, and deploying real-world Generative AI solutions, with a specific focus on GenAI adoption and Agentic AI for business process automation
Proficiency in at least two programming languages. Strong preference for Python, with significant experience in Javascript/Typescript and Golang being highly valued
Demonstrated deep hands-on experience in engineering and deploying enterprise-grade solutions that are highly scalable, resilient, and performant
Strong theoretical and practical understanding of Large Language Models (LLMs), including experience with various open-source and proprietary models, fine-tuning techniques, and deployment strategies
Proficiency with transformers, agentic frameworks, vector stores, and advanced search algorithms
Hands-on experience with Retrieval Augmented Generation (RAG) implementations
Experience with relevant GenAI/ML frameworks such as LangChain, LangGraph, MLFlow, Spring AI, Spring Boot, and Flask
Extensive experience with data analysis and manipulation using tools like SQL and Pandas
Proficiency in database technologies including Oracle, Postgres, or MongoDB
Proven experience in designing and implementing robust REST and WebSocket APIs

Job Responsibility

Strategic Leadership & GenAI Adoption: Drive the identification, evaluation, and adoption of emerging Generative AI technologies and tools to develop innovative solutions and enhance existing platforms. Actively design, develop, and implement AI/ML solutions within financial applications, with a strong emphasis on practical GenAI adoption. This includes hands-on coding, prototyping, and deploying GenAI solutions
Agentic AI for Business Process Automation: Lead the end-to-end design, prototyping, and implementation of Agentic AI solutions focused on automating critical business processes, ensuring they address business needs and scale effectively across the enterprise. This includes designing and implementing Retrieval Augmented Generation (RAG) systems to enhance accuracy and contextuality, especially for financial data
Chatbot Development: Design, develop, and deploy intelligent chatbot solutions capable of interacting with users across both structured data sources and unstructured data, leveraging GenAI for natural language understanding and generation in complex conversational flows
Document Processing & OCR Integration: Implement solutions for processing and extracting information from documents, including integrating Optical Character Recognition (OCR) technologies to handle scanned documents and images, making their content available for GenAI models
Text-to-SQL Implementation: Develop and deploy solutions leveraging Large Language Models to facilitate Text-to-SQL capabilities, enabling users to generate SQL queries from natural language requests, thus simplifying data access and analysis from structured databases
Data Analysis & Feature Engineering: Analyze large, complex datasets, identify intricate patterns, and extract actionable insights, leveraging GenAI capabilities for data augmentation or synthetic data generation, particularly relevant for Agentic AI applications
Data Pipeline Development: Build and maintain robust, scalable data pipelines for data ingestion, processing, and transformation, specifically optimizing for the unique requirements of GenAI model training and inference
Cross-Functional Collaboration: Partner with multiple management teams and business stakeholders to deeply understand requirements and translate them into precise technical specifications and actionable roadmaps, incorporating GenAI and Agentic AI possibilities into discussions
System Enhancements & Architecture: Identify and define necessary system enhancements to deploy new GenAI products and process improvements. Ensure application design adheres to the overall architecture blueprint, integrating GenAI and Agentic AI components seamlessly
Problem Resolution: Resolve a variety of high-impact problems/projects through in-depth evaluation of complex business processes, system processes, and industry standards, applying GenAI and Agentic AI where they can offer innovative solutions

Fulltime

Senior Engineer – Knowledge Graph & Context

We are seeking a visionary Senior Knowledge Graph & Context Engineer for the end...

Location

Egypt , Cairo

Salary:

Not provided

Coca-Cola HBC

Expiration Date

Until further notice

Requirements

BSc or MSc in a relevant field such as Computer Science, Information Science, Data Science, Artificial Intelligence, Knowledge Engineering, Computational Linguistics, or a related discipline
1+ experience in knowledge engineering, context engineering, semantic technologies, enterprise search, GenAI / LLM solutions, or AI product enablement
Strong understanding of one or more of the following: ontologies, taxonomies, controlled vocabularies, knowledge graphs, metadata modelling, semantic enrichment, and information retrieval
Hands-on experience designing or supporting RAG pipelines, prompt / grounding patterns, context orchestration and memory management, context quality and provenance, semantic search, and evaluation of AI responses
Experience with relevant technologies such as graph databases, vector / search platforms, semantic web technologies, and cloud AI / data ecosystems. Exposure to tools and W3C standards such as RDF, OWL, SKOS, SHACL, SPARQL, Neo4j, Stardog, Ontotext GraphDB, Azure ecosystem, or equivalent, is considered a strong advantage
Experience working with enterprise knowledge and data domains such as PLM, PIM / MDM, LIMS, Regulatory, DAM, ERP, SCM, or other structured and unstructured business data sources
Strong influencing, interpersonal, and communication skills, with the ability to work effectively across Data, AI, Technology, and business functions
Experience presenting solutions, trade-offs, and recommendations to senior management and technical leadership teams

Job Responsibility

Design, build, and operationalize the enterprise context layer that supports AI assistants, semantic search, and decision-support solutions across business domains
Transform enterprise knowledge assets into reusable, trusted, and scalable context packs for Agentic AI outcomes
Design and optimize retrieval pipelines, grounding patterns, chunking strategies, metadata enrichment, and prompt/context orchestration to ensure accurate, relevant, and safe AI responses
Partner closely with Ontology Engineers, AI Engineers, and Data teams to apply semantic enrichment, taxonomies, controlled vocabularies, and relationship modelling that improve discoverability, interoperability, and reasoning
Embed policy-aware controls, access rules, governance standards, and quality checks into context pipelines to ensure compliant and trusted use of enterprise knowledge
Work with Data Platform and Engineering teams to productionize context services across graph, search, and AI ecosystems, ensuring performance, scalability, monitoring, and maintainability
Partner with Product, Data Platform, Security / Compliance, Architecture, and business domain teams to align priorities with business needs and accelerate adoption of AI-enabled knowledge solutions
Help improve findability, reuse, decision quality, and business efficiency, while tracking impact, adoption, and value realization
Support the embedding of knowledge-driven and AI-enabled processes into business workflows, tools, and digital products
Drive continuous improvement in knowledge quality, retrieval effectiveness, semantic consistency, and AI response performance

What we offer

Coaching and mentoring programs
Development opportunities
Equal opportunity employer
IT Equipment
Work with iconic brands

Senior Applied AI Engineer

Microsoft is seeking a Senior Applied AI Engineer to join a pioneering team focu...

Location

United States , Redmond

Salary:

119800.00 - 234700.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Computer Science, Engineering, Data Science, Math, Business, or related field AND 4+ years experience in engineering, product/technical program management, data analysis, or product development OR equivalent experience
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role
This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter
8+ years of professional software development experience with at least one of the following: C#, C++, or Java
8+ years of professional experience building cloud applications, preferably full stack
3+ years of experience with GenAI, LLMs, or agentic systems
Experience designing and implementing enterprise-scale services
Experience delivering Dynamics 365 and/or Power Platform solutions is desirable
Experience in agentic AI development and leveraging AI tools for development
Verbal, written, and cross-team collaboration skills

Job Responsibility

Design, build, and ship AI-powered software systems leveraging generative AI, LLMs, and agentic reasoning frameworks
Develop and integrate autonomous AI agents capable of task decomposition, multi-step planning, memory, tool usage, and dynamic decision-making across enterprise workflows
Apply state-of-the-art techniques including foundation models, prompt engineering, retrieval-augmented generation (RAG), multi-agent architectures, and classical machine learning where appropriate
Fine-tune and adapt foundation models using domain-specific data, and evaluate model behavior across relevance, quality, hallucination, bias, latency, and cost
Build rapid prototypes and translate them into production-grade systems, contributing to deployment, debugging, monitoring, and operational excellence
Design and implement evaluation frameworks and automated evaluators, using offline experiments, shadow testing, online experimentation, and telemetry to measure impact and guide iteration
Partner closely with product managers, researchers, and engineers to bring agentic capabilities into Microsoft’s business and productivity platforms
Write high-quality, well-documented, and maintainable code, along with clear technical design specifications
Mentor engineers and contribute to raising the overall engineering and applied AI bar across the team
Apply Responsible AI principles throughout the development lifecycle, proactively identifying and mitigating risks related to fairness, bias, privacy, security, and misuse

Fulltime

Software Engineer, Enterprise

At Scale AI, we’re not just building AI tools—we’re pioneering the next era of e...

Location

United Kingdom , London

Salary:

Not provided

Scale

Expiration Date

Until further notice

Requirements

4+ years of experience developing large-scale backend or infrastructure systems, with a strong emphasis on distributed services, reliability, and scalability
Proficiency in Python or TypeScript, with experience designing high-performance APIs and backend architectures using frameworks such as FastAPI, Flask, Express, or NestJS
Deep familiarity with cloud infrastructure (AWS and Azure preferred), including container orchestration (Kubernetes, Docker) and Infrastructure-as-Code tools like Terraform
Experience managing data systems such as relational and NoSQL databases (PostgreSQL, DynamoDB, etc.) and building pipelines for data-intensive applications
Hands-on experience with GenAI applications, model integration, or AI agent systems—understanding how to deploy, evaluate, and scale AI workloads in production
Strong understanding of observability, CI/CD, and security best practices for running services in enterprise or multi-tenant environments
Ability to balance rapid iteration with production-grade quality, shipping reliable backend systems in fast-paced environments
Collaborative mindset, working closely with ML, infra, and product teams to bring complex GenAI systems into production at enterprise scale

Job Responsibility

Design, build, and scale backend systems that power enterprise GenAI products, focusing on reliability, performance, and deployment across both Scale’s and customers’ infrastructure
Develop core services and APIs that integrate AI models and enterprise data sources securely and efficiently, enabling production-scale AI adoption
Architect scalable distributed systems for data processing, inference, and orchestration of large-scale GenAI workloads
Optimize backend performance for latency, throughput, and cost—ensuring AI applications can operate at enterprise scale across hybrid and multi-cloud environments
Manage and evolve cloud infrastructure (AWS, Azure, or GCP), driving automation, observability, and security for large-scale AI deployments
Collaborate with ML and product teams to bring cutting-edge GenAI models into production through efficient APIs, model serving systems, and evaluation frameworks
Continuously improve reliability and scalability, applying strong engineering practices to make AI systems robust, maintainable, and enterprise-ready

Staff Applied AI Engineer, Enterprise GenAI

The SGP ML team works on the front lines of this AI revolution. We interface dir...

Location

United States , San Francisco, CA; Seattle, WA; New York, NY

Salary:

216000.00 - 270000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

7+ years of full-time engineering experience, post-graduation
A love for solving deeply complex technical problems with ambiguity using state of the art research and AI to accomplish your client's business goals
Strong engineering background: a Bachelor's degree in Computer Science, Mathematics, or another quantitative field or equivalent strong engineering background
Deep familiarity with a data-driven approach when iterating on machine learning models and how changes in datasets can influence model results
Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
Proficiency in Python to write, test and debug code using common libraries (ie numpy, pandas)

Job Responsibility

Own, plan, and optimize the AI behind our Enterprise customer's deepest technical problems
Leverage SGP to build the most advanced AI agents across the industry including multimodal functionality, tool-calling, and more
Have experience gathering business requirements and translating them into technical solutions
Meet regularly with customer teams onsite and virtually, collaborating cross-functionally with all teams responsible for their data and ML needs
Push production code in multiple development environments, writing and debugging code directly in both our customer's and Scale's codebases
Be able and willing to multi-task and learn new technologies quickly

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
commuter stipend

Fulltime

Senior AI Engineer

The Senior AI Engineer at NTT DATA will be responsible for designing and deployi...

Location

South Africa , Johannesburg

Salary:

Not provided

NTT DATA

Expiration Date

Until further notice

Requirements

Postgraduate level qualification (Honours or Masters) in Computer Science, Data Science, Engineering, Mathematics, Statistics, Economics or a related field
Alternatively, equivalent practical experience delivering AI or machine learning solutions in enterprise environments
Strong proficiency in Python for AI and machine learning development
Solid understanding of machine learning fundamentals and evaluation techniques
Hands-on experience with ML frameworks such as scikit-learn, PyTorch, or TensorFlow
Experience working with Generative AI and LLMs in practical use cases
Strong data handling skills, including SQL and data querying
Experience deploying models or AI services into production environments
Experience with vector databases and embeddings (e.g., for RAG architectures)
Experience with cloud platforms (Azure preferred

Job Responsibility

Design and develop Generative AI solutions, including LLM-based applications
Implement techniques such as prompt engineering, Retrieval-Augmented Generation (RAG), and fine-tuning
Develop and evaluate machine learning models across supervised, unsupervised, and NLP use cases
Optimise model performance, reliability, and cost efficiency for enterprise environments
Work with structured and unstructured data sources (databases, APIs, documents, transcripts, logs)
Collaborate with data engineers to support data ingestion, preparation, and transformation pipelines
Ensure AI solutions are scalable, maintainable, and aligned with software engineering best practices
Package and deploy AI and GenAI solutions into production (APIs, services, batch workflows)
Support cloud-based deployments, primarily on Microsoft Azure, including Azure OpenAI and Azure AI services
Apply MLOps and LLMOps practices such as versioning, monitoring, evaluation, and continuous improvement

Fulltime

Select Country

Applied AI Engineer, Enterprise GenAI

Scale

Location:
United States , San Francisco ▼
New York

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Nice to have:

Additional Information:

Job Posted:
February 20, 2026

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