AI ML Software Engineering PMTS

Salesforce; Bengaluru; On-site; INR 20,00,000 - 30,00,000; Full time

Job summary

A leading technology company is seeking a Principal Engineer to own the architecture and execution of AI/ML deployment systems. The successful candidate will lead teams, optimize infrastructure for model serving, and implement cutting-edge MLOps practices. Applicants should have extensive experience in software engineering and AI/ML systems, along with strong leadership and mentoring skills. This role offers a challenging opportunity to innovate in a rapidly evolving tech landscape.

Qualifications

• 15+ years of software engineering experience; 7+ years building and operating AI/ML systems at scale.
• Expertise in at least one object-oriented programming language and one ML native language.
• Strong experience in Applied AI, focusing on operationalizing deployment vehicles.

Responsibilities

• Lead the architectural vision for the ML serving platform.
• Design low-latency, high-throughput model serving infrastructure.
• Work with product teams to translate user needs into technical requirements.

Skills

Software engineering experience

AI/ML systems expertise

Object-oriented programming

Applied AI experience

ML serving frameworks knowledge

Deep mastery of system design

Strong communication skills

Mentorship abilities

Job description

Overview

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Role Description

As a Principal Engineer on the Agentforce Deployment Platform team, you will own the end-to-end architecture, strategy, and execution of our AI/ML deployment and operationalization systems. You'll collaborate closely with software engineers, data scientists, product managers, and data teams to build and turn cutting-edge architecture and research into scalable, highly available, and compliant production-ready systems. You are not just a coder - you are a thought leader, innovator, builder and mentor who thrives on ownership and pushing boundaries in production MLOps, AI infrastructure, and reliable delivery in a rapidly changing and cutting edge space.

Key Responsibilities

• Lead the architectural vision for our global-scale ML serving, inference, and model management platform.
• Design and optimize low-latency, high-throughput model serving infrastructure and data flow for training and inference at scale.
• Strategize and implement AI assisted migration platform that is proactive governance and reactive autonomous remediation by enforcing policies at every stage for Deployment lifecycle.
• Work with product and business teams to translate user needs into technical requirements, focusing on platform capabilities for rapid iteration and secure deployment.
• Set long-term technical strategy and direction, serving as a top-tier technical mentor for engineers across teams.
• Drive adoption of cutting-edge MLOps best practices for model training, secure and automated deployment, proactive monitoring, and robust governance.
• Innovate not just in model building, but in how models are packaged, delivered, and operated in a mission-critical environment.
• Make strategic technical decisions on build vs buy, model selection, and core platform infrastructure to ensure scalability and cost-efficiency.

Required Skills

• 15+ years of software engineering experience; 7+ years building and operating AI/ML systems at scale.
• Demonstrable Principal-level impact and ownership on large-scale engineering initiatives.
• Expertise in at least one object-oriented programming language (Java/C++/GoLang) and one ML native language (Python).
• Strong experience in Applied AI, specifically focusing on the infrastructure and platform services required to operationalize deployment vehicles effectively.
• Deep experience with high-scale ML serving frameworks (e.g., TorchServe, TensorFlow Serving, NVIDIA Triton).
• Familiarity with LLMs, vector databases, and applied generative AI deployment patterns (e.g., containerization, traffic management, and cost optimization of RAG pipelines).
• Deep mastery of system design, distributed systems, and cloud-native architectures (AWS/GCP, Kubernetes, Service Mesh).
• Exceptional track record in building and scaling ML serving pipelines, real-time inference systems, and API platforms.
• Proven ability to influence and drive technical consensus across cross-functional teams and mentor senior engineers.
• Strong communication and collaboration skills across technical and non-technical teams.
• Ability to translate complex AI concepts into pragmatic and compliant engineering decisions.
• Experience in startups or high-growth tech companies.

Preferred Skills

• Contributions to open-source AI/ML infrastructure or MLOps projects.
• Patents, papers, blogs, or other external publications related to large-scale ML deployment, observability, or governance.
• Strong platform and product-centric mindset demonstrated by high-leverage infrastructure projects

Accommodations

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Posting Statement

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that's inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications - without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, assessment of job performance, discipline, termination, and everything in between.