Senior AI-Enabled Software Engineer responsible for designing and running a delivery model for AI-enabled software delivery, partnering with product owners, architects, and engineering teams.
Responsibilities
Design pod structure and handoffs across the software lifecycle
Track advancements in AI-enabled software delivery and share updates with engineering and leadership
Build tooling that connects agents to repositories, test environments, and review workflows
Establish quality gates, security review, and traceability for agent-produced work
Ensure delivery standards and traceability extend to agent-produced work on the advisor platform
Requirements
Nice to have: internal developer platforms
Nice to have: highly regulated industry experience
Lead the design, adoption, and scaling of Generative and Agentic AI ecosystems for strategic clients. This role blends advisory, hands-on solutioning, and practice-building leadership to ensure AI delivers tangible, industry-specific business outcomes.
Responsibilities
Architect and implement modular, scalable AI platforms that integrate Large Language Models (LLMs), Small Language Models (SLMs), and multi-agent orchestration.
Serve as the anchor architect for major client accounts and lead C-level discussions to define AI roadmaps.
Envision, design, and deliver verticalized AI use cases across various industries including Banking, Healthcare, Retail, and Manufacturing.
Lead end-to-end model lifecycle: data preparation, training, fine-tuning, deployment, monitoring, and continuous improvement.
Establish enterprise standards for MLOps/LLMOps pipelines and build agent lifecycle governance frameworks.
Design, build, and evolve the MDASH scanning engine, integrating AI agents and models to analyze codebases, detect vulnerabilities, and generate fixes.
Locations (3)
Redmond, Washington, United States
San Francisco, California, United States
New York, New York, United States
Compensation
Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
Responsibilities
Design, build, and evolve the MDASH scanning engine, including agent orchestration, multi-model routing, code understanding, vulnerability detection, reachability validation, and fix generation and verification.
This role represents a rare opportunity to define and build the AI capabilities of a newly standalone company (DePuy Synthes). You will shape how generative and agentic AI is designed, governed, reused and scaled, while leading a team that turns emerging technologies and priority business needs into enterprise platforms, accelerators and strategic applications.
Locations (2)
Raritan, New Jersey, United States
Bengaluru, Karnataka, India
Compensation
The anticipated base pay range for this position is $140,000.00 - $241,500.00
Responsibilities
Define and evolve the enterprise AI strategy, architecture and roadmap across Databricks Mosaic AI, Amazon Bedrock, AgentCore, the enterprise model gateway and related services.
Architect the agentic runtime, including agent orchestration and mesh, MCP tool and skill registries, Agent-to-Agent interoperability, agent identity, entitlements and secure tool execution.
Design reusable context and knowledge services, including RAG and Graph RAG, vector stores, knowledge graphs such as Amazon Neptune, shared memory and context assembly.
Lead multiple engineering teams developing and deploying an enterprise LLM-powered platform that supports analysts, engineers, and operators across a variety of mission domains.
Compensation
Salary range: $205,000 - $280,000
Responsibilities
Solicit requirements and architect an enterprise agentic platform designed to power intelligent, autonomous workflows
Work directly with stakeholders, engineers, and cyber analysts to understand operational workflows and identify high-impact automation opportunities
Evaluate AI models, deployment architectures, and infrastructure options to determine the optimal balance of capability, performance, and cost
Advise customers on AI investment strategies and resource allocation to maximize mission impact and operational effectiveness
Stay current on emerging AI technologies, industry trends, and best practices, advocating for the adoption of innovative capabilities where appropriate
Lead the enterprise AI strategy and architecture, AI Innovation Lab, and a distributed team of AI architects and engineers across US and GCC locations.
Compensation
The anticipated base pay range for this position is : $140,000.00 - $241,500.00
Responsibilities
Define and evolve the enterprise AI strategy, architecture and roadmap
Architect the agentic runtime and related services
Design reusable context and knowledge services
Establish patterns for LLMOps and MLOps
Create a discoverable catalog of AI services
Requirements
Bachelor's degree in Computer Science, Engineering or a related technical field
The role requires translating business processes and data structures into skills and instructions that an agentic system can execute, then connecting these skills to other agents, tools, and organizational systems.
Responsibilities
Comprendre et modeliser les processus d’affaires
Travailler avec les équipes d’affaires pour documenter les processus, les règles de décision, les exceptions et les données utilisées
Déterminer les étapes qu’un agent peut prendre en charge, celles qui demandent une validation humaine et celles qui doivent rester manuelles
Traduire les structures de données (documents, tables, référentiels, API) en contexte exploitable par le système agentique
Concevoir les skills et instructions des agents
Requirements
Formation ou expérience en développement logiciel ou en ingénierie IA
Own the agent layer of the agentic framework — the build, not the design. Run the agent development pipeline: LLM integration, prompt-to-action engineering, agent evaluation and testing, and the reusable agent components the rest of the function depends on.
Locations (3)
United Kingdom
India
United States
Responsibilities
Own the agent layer: agent development pipeline, LLM integration, prompt-to-action engineering and agent lifecycle.
Own agent evaluation and testing, including credible approaches to non-deterministic output.
Build GenAI security use cases such as alert triage automation and threat summarisation.
Develop reusable agent components that reduce effort on the next build.
Build to AI Architecture's specifications and raise design problems rather than engineering around them.
Senior technical leadership role within the Enterprise AI team, focused on setting standards for AI-native development and owning delivery outcomes for strategic business units.
Extend the engine's coverage across vulnerability classes, programming languages, frameworks, build systems, and codebase sizes, from single services to large monorepos.
Integrate program analysis techniques into agent workflows to make detections more precise and grounded.
Improve the engine's precision, recall, consistency, and fix quality, working from evaluation results, research findings, and production feedback.
Optimize scan performance, scalability, cost, and latency so MDASH works well on enterprise-scale codebases and in CI/CD workflows.
Requirements
Bachelor's Degree in Computer Science or related technical field
Master's Degree in Computer Science or related technical field
Nice to have: Master's Degree in Computer Science or related technical field
Nice to have: 8+ years technical engineering experience
Nice to have: 12+ years technical engineering experience
Establish patterns for LLMOps and MLOps, observability, evaluation, Responsible AI, security and AI cost management in partnership with platform and governance teams.
Create a discoverable catalog of AI services, models, agents, tools, skills and reference patterns that teams can reuse across the enterprise.
Requirements
Bachelor's degree in Computer Science, Engineering or a related technical field
Define and own enterprise-level development standards for AI solutions across GSK — reference architectures, agentic delivery-automation workflows, delivery bot meshes, evaluation frameworks, and handoff hygiene.
Set the enterprise technical direction for AI-native engineering, establishing how agentic workflows and delivery bot meshes are designed, secured, monitored, and reused at scale.
Own the enterprise pattern library and reference platform, ensuring reusable agents, prompt templates, MCP integrations, and reference architectures.
Chair FDE technical design review and contribute to Enterprise AI architecture governance.
Own FDE delivery for a defined portfolio of strategic business units — engagement pipeline, prioritization, quality, and outcomes from intake to clean handoff.
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