Senior Lead Engineer – AI Platform Architecture & Engineering

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Company:

Qualcomm India Private Limited

## Job Area:

Engineering Group, Engineering Group > Software Engineering

General Summary:

We are seeking an experienced and highly motivated Senior Lead Engineer to lead the design, deployment, operationalization, and scaling of Generative AI (GenAI) solutions including orchestrator framework and deployment across enterprise and product environments.

The successful candidate will combine deep software engineering expertise with hands-on experience in building Agents, orchestrators, LLM-based applications, MLOps, cloud-native architectures, and production deployment of AI services. This individual will drive technical strategy, mentor engineering teams, architect scalable AI platforms, and ensure successful delivery of AI-powered solutions from concept through production.

This role requires strong leadership capabilities, exceptional technical depth, and a proven track record of deploying AI systems that deliver measurable business value.

Minimum Qualifications:

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.

OR

PhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.

• 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.

Job Summary

Preferably looking for master's or Ph.D. with focus on deployment and application AI.

Key Responsibilities

AI Platform Architecture & Deployment

• Lead design and implementation of scalable AI/ML platforms, building agents, orchestrator frameworks and enterprise AI infrastructure.

• Architect and deploy production-grade Generative AI, Machine Learning, and Agentic AI solutions.

• Build secure and reliable AI deployment pipelines and operational frameworks.

• Design AI services capable of supporting enterprise-scale workloads and high availability.

• Drive adoption of cloud-native AI technologies and modern deployment architectures.

AI/ML Solution Development

• Develop end-to-end AI systems utilizing:

• Large Language Models (LLMs)

• Retrieval-Augmented Generation (RAG)

• AI Agents and Multi-Agent Systems

• Machine Learning and Deep Learning frameworks

• NLP and Conversational AI

• Computer Vision

• Recommendation Systems

• Predictive Analytics

• Evaluate and select appropriate models, architectures, and deployment strategies.

MLOps & AI Operations

• Establish and maintain MLOps best practices.

• Implement:

• CI/CD pipelines for AI workloads

• Model versioning

• Experiment tracking

• Automated retraining

• Model monitoring

• Drift detection

• Performance benchmarking

• Governance and auditability

• Ensure reliable deployment, operational monitoring, and lifecycle management of AI solutions.

Engineering Leadership

• Provide technical leadership across AI initiatives.

• Lead architecture reviews and technical design discussions.

• Mentor engineers and data scientists.

• Establish engineering standards, best practices, and reusable frameworks.

• Collaborate with senior leadership on AI strategy and roadmaps.

Product & Cross-Functional Collaboration

• Partner with Product Management, Architecture, Security, IT, Data Engineering, and Business stakeholders.

• Translate business requirements into scalable technical solutions.

• Drive adoption of AI technologies throughout the software development lifecycle.

AI Governance, Security & Compliance

• Ensure responsible AI practices and model governance.

• Implement security controls for AI systems and deployed models.

• Develop standards for privacy, compliance, explainability, and risk management.

• Maintain governance frameworks for enterprise AI deployments.

Required Qualifications

Education

• Bachelor's or Master's degree in:

• Computer Science

• Artificial Intelligence

• Machine Learning

• Software Engineering

• Data Science

• Electrical Engineering

• Related technical discipline

Experience (5-7 Years)

• 5+ years of software engineering experience.

• 3+ years of hands-on AI/ML engineering experience.

• 3+ years leading technical teams or major technical programs.

• Proven experience deploying AI systems into production environments.

Technical Expertise

Programming

Strong proficiency in:

• Python

• C/C++

AI/ML Frameworks

Experience with:

• PyTorch

• TensorFlow

• Keras

• Hugging Face

• ONNX

• Scikit-learn

Generative AI

Demonstrated expertise in:

• LLM architectures

• Prompt Engineering

• Context Engineering

• RAG systems

• Fine-tuning methodologies

• Agentic AI frameworks

• Function Calling

• Tool Use

• Multi-agent orchestration

AI Infrastructure

Experience with:

• Kubernetes

• Docker

• Distributed computing

• GPU acceleration

• High-performance AI inference

• AI serving frameworks

MLOps

Hands-on experience with:

• MLFlow

• Kubeflow

• Airflow

• Vertex AI

• SageMaker

• Azure Machine Learning

• CI/CD automation

• Model monitoring platforms

Cloud Platforms

Experience deploying solutions on:

• Microsoft Azure

• AWS

• Google Cloud Platform

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