Technical Project Manager – Enterprise AI

Emergys Corp.; Pune District; On-site; INR 3,500,000 - 6,500,000; Full time

Job summary

Emergys Corp. is seeking a senior Solutions Architect / ML Systems Engineer to own the end-to-end architecture and delivery of complex ML/LLM-based solutions.

The role requires breadth across application architecture, cloud, infrastructure, performance, security, and enterprise deployments. Responsibilities include designing architectures for on-prem and cloud, coordinating with customers and engineering teams, and ensuring non-functional requirements are met.

Qualifications

• Hands-on experience in designing enterprise ML/LLM solutions.
• Strong knowledge of ML architecture, security and observability.
• Experience deploying ML models at scale in cloud or on-prem environments.

Responsibilities

• Own end-to-end technical architecture and solution delivery for ML/LLM projects.
• Design multiple architectures based on scalability, security and performance needs.
• Collaborate with customers and engineers to translate requirements into scalable solutions.
• Define approaches for development, deployment, testing, and operations.
• Ensure quality standards, KPIs, and engineering practices are followed.
• Provide on-call support and drive optimization across deployments.

Skills

ML Systems Engineering

MLOps

LLMOps

Tools

Kubernetes

CI/CD

DevOps

Job description

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.

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.