Lead the design and delivery of AI-enabled solutions for building, test, and release workflows of software products using approved enterprise technologies and enterprise-ready solution patterns.

Identify engineering workflows that can be fully or partially offloaded to AI systems, reducing or eliminating repeated manual engineering effort.

Design and implement scalable AI-powered solutions in engineering workflows such as build and test triage, failure analysis, flaky test intelligence, debugging assistance, and release decision support.

Work hands-on with engineering teams to turn ideas into working solutions that can be rolled out in production.

Own key initiatives from concept and architecture through development, rollout, validation, adoption, and ongoing improvement.

Help define technical direction, propose practical architecture improvements, and influence how AI is introduced into critical engineering systems.

Develop tools, supporting systems, and infrastructure that improve engineering productivity and enable sustainable AI adoption.

Review technical designs and implementations, provide strong feedback, and mentor junior engineers.

Establish practical approaches for evaluation, telemetry, monitoring, observability, and continuous improvement of AI-enabled systems.

Design solutions that account for operational constraints such as rate limits, retry behavior, error handling, and production supportability.

Collaborate with internal teams, domain experts, and junior talent to build solutions that can scale across the organization.

Ensure all automation and intelligence align with Emerson’s reliability, safety, and regulatory commitments

Act as a key technical leader, shaping the long-term roadmap for AI driven DevOps transformation across T&M. • Partner with engineering leaders, architects, and operational teams to drive adoption of intelligent automation and data driven decision-making.

Mentor teams on AI/ML fundamentals, emerging practices, and how to leverage automation to enhance product quality and operational performance.

You are an application-focused AI engineering leader who enjoys solving real workflow and engineering problems.

You are comfortable operating as both an architect and a builder.

You can take an ambiguous problem, create a practical solution approach, and drive it through execution.

You understand what it takes to deploy systems at an organizational scale, including rollout, adoption, governance, observability, and long-term maintainability.

You work well across teams, influence technical direction, and help raise the capability of the engineers around you.

You are self-driven, pragmatic, and able to make sound trade-offs in evolving situations.

B.E / B.Tech / M.Tech / MCA in Computer Science / Electronics / Electrical / Instrumentation / Artificial Intelligence / Machine Learning / related technical discipline.

8+ years of proven experience in software engineering, systems design, AI application engineering, or related technical roles. Minimum 3+ years experience developing AI based applications.

Hands-on experience with retrieval-augmented generation architectures, workflow automation, evaluation methods, platform integration, agentic systems, cost-aware AI design, and operationalizing AI-enabled systems in production, including monitoring, observability, feedback loops, and lifecycle management.

Strong understanding of AI training fundamentals, including how models are developed, adapted, evaluated, and improved, even if your primary industry experience has been in building and deploying AI-enabled applications rather than training foundation models directly.

Strong experience building AI-enabled applications or workflow solutions in enterprise environments.

Strong understanding of DevOps and modern software delivery systems, including CI/CD pipelines, build and release workflows, automation, observability, and production operational considerations.

Proven experience designing, deploying, and operating systems or workflow solutions on an organizational scale, including rollout across multiple teams or large engineering groups.

Strong problem-solving ability, including diagnosing technical issues and proposing practical, high-quality solutions.

Good understanding of enterprise constraints such as governance, approved technologies, security, observability, and production-readiness.

Ability to work effectively with engineering teams that have deep domain expertise but limited AI maturity.

Experience applying AI to build systems, test systems, CI/CD workflows, release engineering, software delivery, and engineering productivity workflows.

Experience with enterprise AI platforms, internal developer platforms, or similar shared engineering platforms used to operationalize AI solutions at scale.

Familiarity with Agile software development practices, CI pipelines, and workflow automation.

Understanding of NI software tools or related internal engineering ecosystems.

Exposure to fine-tuning, model adaptation, data preparation, evaluation pipelines, or other AI training-related workflows.

A strong track record of driving large, ambiguous technical initiatives to successful completion.

Ability to work effectively in a diverse, multi-cultural engineering environment and communicate well with both engineering teams and management.