Software Architect

Job Description

Job Summary -

Bosch Research is seeking a highly accomplished and technically authoritative Software Expert in AI/ML Architecture to define, evolve, and lead the technical foundations of enterprise-grade, AI-driven systems. This is a technical leadership role without people management responsibilities, intended for professionals with deep expertise in software architecture, AI/ML systems, and large-scale engineering applications and their end-to-end deliveries.

You will own the architecture and technical delivery of complex software solutions—ensuring they are robust, scalable, and capable of serving diverse business domains and datasets. The ideal candidate demonstrates mastery in cloud-native engineering, MLOps, Azure ML, and the integration of AI Algorithms (Computer Vision, Text, Timeseries, ML, etc.), LLMs, Agentic AI, and other advanced AI capabilities into secure and high-performing software environments

Roles & Responsibilities:

Technical Architecture and Solution Ownership

• Define, evolve, and drive software architecture for AI-centric platforms across industrial and enterprise use cases.

• Architect for scalability, security, availability, and multi-domain adaptability, accommodating diverse data modalities and system constraints.

• Embed non-functional requirements (NFRs)—latency, throughput, fault tolerance, observability, security, and maintainability—into all architectural designs.

• Incorporate LLM, Agentic AI, and foundation model design patterns where appropriate, ensuring performance and operational compliance in real-world deployments.

Enterprise Delivery and Vision

• Lead the translation of research and experimentation into production-grade solutions with measurable impact on business KPIs (both top-line growth and bottom-line efficiency).

• Perform deep-dive gap analysis in existing software and data pipelines and develop long-term architectural solutions and migration strategies.

• Build architectures that thrive under enterprise constraints, such as regulatory compliance, resource limits, multi-tenancy, and lifecycle governance.

AI/ML Engineering and MLOps

• Design and implement scalable MLOps workflows, integrating CI/CD pipelines, experiment tracking, automated validation, and model retraining loops.

• Operationalize AI pipelines using Azure Machine Learning (Azure ML) services and ensure seamless collaboration with data science and platform teams.

• Ensure architectures accommodate responsible AI, model explainability, and observability layers.

Software Quality and Engineering Discipline

• Champion software engineering best practices with rigorous attention to:

- Code quality through static/dynamic analysis and automated quality metrics

- Code reviews, pair programming, and technical design documentation

- Unit, integration, and system testing, backed by frameworks like pytest, unit test, or Robot Framework

- Code quality tools such as SonarQube, CodeQL, or similar

• Drive the culture of traceability, testability, and reliability, embedding quality gates into the development lifecycle.

• Own the technical validation lifecycle, ensuring reproducibility and continuous monitoring post-deployment.

Cloud-Native AI Infrastructure

• Architect AI services with cloud-native principles, including microservices, containers, and service mesh.

• Leverage Azure ML, Kubernetes, Terraform, and cloud-specific SDKs for full lifecycle management.

• Ensure compatibility with hybrid-cloud/on-premise environments and support constraints typical of engineering and industrial domains

Qualifications

Educational qualification:

Masterís or Ph.D. in Computer Science, AI/ML, Software-Engineering, or a related technical discipline

Experience:

• 15+ years in software development, including:

• Deep experience in AI/ML-based software systems

• Strong architectural leadership in enterprise software design

• Delivery experience in engineering-heavy and data-rich environments

Mandatory/requires Skills:

• Programming: Python (required), Java, JS, Frontend/Backend Technologies, Databases C++ (bonus)

• AI/ML: TensorFlow, PyTorch, ONNX, scikit-learn, MLFlow(equivalents)

• LLM/GenAI: Knowledge of transformers, attention mechanisms, fine-tuning, prompt engineering

• Agentic AI: Familiarity with planning frameworks, autonomous agents, and orchestration layers

• Cloud Platforms: Azure (preferred), AWS or GCP; experience with Azure ML Studio and SDKs

• Data & Pipelines: Airflow, Kafka, Spark, Delta Lake, Parquet, SQL/NoSQL

• Architecture: Microservices, event-driven design, API gateways, gRPC/REST, secure multi-tenancy

• DevOps/MLOps: GitOps, Jenkins, Azure DevOps, Terraform, containerization (Docker, Helm, K8s)

What You Bring

• Proven ability to bridge research and engineering in the AI/ML space with strong architectural clarity.

• Ability to translate ambiguous requirements into scalable design patterns.

• Deep understanding of the enterprise SDLCóincluding review cycles, compliance, testing, and cross-functional alignment.

• A mindset focused on continuous improvement, metrics-driven development, and transparent technical decision-making.