Technical Project Manager – Enterprise AI
- India
نُشرت أول أمس
عن الوظيفة
Experience: 10-15 years
Location: Pune, India
Job Summary
We are looking for a highly experienced Solutions Architect / ML Systems Engineer responsible for owning the end-to-end technical architecture and delivery of complex ML/LLM-based solutions. The role requires strong breadth across application architecture, ML/LLM systems, cloud, infrastructure, performance engineering, security, and enterprise deployments.
Key Responsibilities
Own the end-to-end technical architecture and solution design for enterprise-grade ML/LLM solutions.
Design multiple solution architectures based on business, technical, scalability, security, and performance requirements.
Ability to co-work with Agentic AI model, Optimizing processes agnatically [agentic optimizing engineering].
Define technical approaches covering application development, infrastructure, deployment, testing, and operational considerations.
Work closely with customers and engineering teams to understand requirements and translate them into scalable technical solutions.
Design solutions for both on-premises/data-center and public-cloud environments.
Architect containerized deployments and cloud-native solutions.
Define and address non-functional requirements, including performance, scalability, availability, reliability, and security.
Provide technical ownership across the complete solution lifecycle, from initial architecture through implementation and deployment.
Evaluate technology choices, architecture patterns, and deployment models.
Work on performance engineering and performance optimization of ML/LLM systems.
Ensure appropriate quality standards, testing approaches, KPIs, and engineering practices are incorporated into the solution.
Support multiple enterprise solution implementations and pilots.
Must be available for on-call support as needed
Ability to collaborate effectively with Agentic AI models
Capability to optimize repetitive processes and workflows using agentic/autonomous engineering approaches
Required Technical Skills
Strong hands-on experience with ML Systems Engineering / MLOps / LLMOps.
Practical experience deploying and operating LLM applications at scale.
Strong understanding of RAG, AI/LLM application architecture, prompt injection, security, guardrails, and observability.
Experience with LLM infrastructure and supporting technologies.
Strong understanding of containers, Kubernetes, CI/CD and DevOps practices.
Experience with public cloud platforms such as AWS, Azure or GCP.
Understanding of on-premises, cloud, hybrid-cloud and data-center deployments.
Strong knowledge of performance engineering and non-functional requirements.
Understanding of enterprise-grade security and deployment considerations.
Preferred / Differentiating Experience
Experience working on B2B enterprise products/platforms.
Experience with products supporting both data-center and cloud deployment models.
Exposure to AI accelerators / AI hardware.
Experience working with semiconductor/AI platforms or simulation environments.
Experience designing solutions for enterprise customers.
Soft Skills
Excellent customer-facing and stakeholder-management skills.
Strong architectural thinking and problem-solving ability.
Ability to communicate complex technical concepts to technical and business stakeholders.
Strong ownership and ability to drive solutions independently.
Location: Pune, India
Job Summary
We are looking for a highly experienced Solutions Architect / ML Systems Engineer responsible for owning the end-to-end technical architecture and delivery of complex ML/LLM-based solutions. The role requires strong breadth across application architecture, ML/LLM systems, cloud, infrastructure, performance engineering, security, and enterprise deployments.
Key Responsibilities
Own the end-to-end technical architecture and solution design for enterprise-grade ML/LLM solutions.
Design multiple solution architectures based on business, technical, scalability, security, and performance requirements.
Ability to co-work with Agentic AI model, Optimizing processes agnatically [agentic optimizing engineering].
Define technical approaches covering application development, infrastructure, deployment, testing, and operational considerations.
Work closely with customers and engineering teams to understand requirements and translate them into scalable technical solutions.
Design solutions for both on-premises/data-center and public-cloud environments.
Architect containerized deployments and cloud-native solutions.
Define and address non-functional requirements, including performance, scalability, availability, reliability, and security.
Provide technical ownership across the complete solution lifecycle, from initial architecture through implementation and deployment.
Evaluate technology choices, architecture patterns, and deployment models.
Work on performance engineering and performance optimization of ML/LLM systems.
Ensure appropriate quality standards, testing approaches, KPIs, and engineering practices are incorporated into the solution.
Support multiple enterprise solution implementations and pilots.
Must be available for on-call support as needed
Ability to collaborate effectively with Agentic AI models
Capability to optimize repetitive processes and workflows using agentic/autonomous engineering approaches
Required Technical Skills
Strong hands-on experience with ML Systems Engineering / MLOps / LLMOps.
Practical experience deploying and operating LLM applications at scale.
Strong understanding of RAG, AI/LLM application architecture, prompt injection, security, guardrails, and observability.
Experience with LLM infrastructure and supporting technologies.
Strong understanding of containers, Kubernetes, CI/CD and DevOps practices.
Experience with public cloud platforms such as AWS, Azure or GCP.
Understanding of on-premises, cloud, hybrid-cloud and data-center deployments.
Strong knowledge of performance engineering and non-functional requirements.
Understanding of enterprise-grade security and deployment considerations.
Preferred / Differentiating Experience
Experience working on B2B enterprise products/platforms.
Experience with products supporting both data-center and cloud deployment models.
Exposure to AI accelerators / AI hardware.
Experience working with semiconductor/AI platforms or simulation environments.
Experience designing solutions for enterprise customers.
Soft Skills
Excellent customer-facing and stakeholder-management skills.
Strong architectural thinking and problem-solving ability.
Ability to communicate complex technical concepts to technical and business stakeholders.
Strong ownership and ability to drive solutions independently.