AI Architect – Generative AI, ML, Cloud & MLOps
- Bengaluru - Bellandur (GTP)
- Full-time
- Posted
Skills
Job description
Job Summary
Synechron is seeking an experienced Generative AI Architect to lead the design, development, and implementation of enterprise-scale Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions.
The role will define AI architecture strategies aligned with business and technology objectives, design secure and scalable AI platforms, and guide solutions across data ingestion, feature engineering, model development, deployment, monitoring, and MLOps.
The position will contribute to business objectives by translating complex business requirements into practical AI-driven solutions, enabling responsible innovation, improving operational efficiency, and establishing reusable architecture standards for enterprise AI initiatives.
Software Requirements
Required Software Skills
•Python: Strong professional experience for AI/ML application development, automation, data processing, model integration, and production services.
•AI/ML frameworks: Strong expertise with TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain, LlamaIndex, or equivalent frameworks.
•Large Language Models: Experience working with OpenAI, Azure OpenAI, Claude, Gemini, or open-source models.
•RAG technologies: Strong understanding of Retrieval-Augmented Generation architectures, embeddings, vector databases, semantic search, retrieval, reranking, and evaluation.
•Prompt engineering: Experience designing, testing, optimizing, and governing prompts for enterprise AI applications.
•AI orchestration: Experience with orchestration frameworks, model integration, workflow coordination, tool calling, and AI application architecture.
•MLOps and LLMOps: Expertise in model lifecycle management, CI/CD, deployment automation, model monitoring, AI observability, evaluation, and operational support.
•Cloud platforms: Experience with Azure, AWS, or Google Cloud Platform.
•Data engineering: Knowledge of data lakes, distributed processing, data pipelines, APIs, microservices, and enterprise data architecture.
•AI governance and security: Strong understanding of AI security, governance, privacy, compliance, explainability, ethical AI, and responsible AI principles.
•Architecture tools and practices: Experience producing solution architectures, technical designs, architecture standards, design patterns, assessments, and review documentation.
Preferred Software Skills
•Agentic AI frameworks and multi-agent architectures.
•Azure AI Foundry, Azure Machine Learning, Databricks, Snowflake, or similar platforms.
•AI-related certifications from Microsoft, AWS, Google, or equivalent platforms.
•Tools supporting data lineage, model governance, experiment tracking, observability, and automated evaluation.
•Technologies supporting enterprise digital transformation, conversational AI, knowledge management, and intelligent automation.
Version and Experience Guidance
•Specific software versions are not mandated in the requirements.
•Candidates should have experience with current, production-supported versions of Python, AI/ML frameworks, cloud services, LLM platforms, vector databases, and MLOps/LLMOps tools.
•Candidates should be able to assess technology compatibility, manage platform evolution, define upgrade strategies, and support lifecycle management.
•Experience should include enterprise-scale architecture and production implementation rather than only experimental or proof-of-concept work.
Overall Responsibilities
•Define and drive the AI architecture strategy aligned with business and technology objectives.
•Design end-to-end AI/ML, Generative AI, and Agentic AI solutions for enterprise applications.
•Architect scalable, secure, high-performance AI platforms using cloud-native technologies.
•Lead solution design across data ingestion, feature engineering, model development, deployment, monitoring, and MLOps.
•Evaluate and recommend AI frameworks, LLMs, vector databases, orchestration tools, and cloud AI services.
•Design and implement RAG, intelligent agent, conversational AI, and knowledge management solutions.
•Establish architecture standards, design patterns, reference architectures, and engineering best practices for AI initiatives.
•Ensure compliance with AI governance, security, privacy, explainability, and responsible AI principles.
•Collaborate with business stakeholders, product teams, data scientists, engineers, security teams, and enterprise architects.
•Provide technical leadership, architectural guidance, mentoring, and design support to development and AI engineering teams.
•Conduct architecture reviews, solution assessments, technology evaluations, and risk reviews.
•Support proposal development, stakeholder discussions, solution presentations, and AI-led transformation initiatives.
•Define success measures for AI solutions, including quality, reliability, scalability, security, cost, latency, adoption, and business impact.
•Promote reusable AI capabilities, standardized delivery practices, and responsible technology adoption.
•Support sustainable AI architecture through efficient model selection, resource utilization, workload optimization, and cost-aware cloud design.
Technical Skills (By Category)
Programming Languages
Essential
•Strong Python development experience.
•Ability to build AI/ML services, data-processing workflows, APIs, integrations, and automation components.
•Ability to write modular, maintainable, testable, secure, and production-ready code.
•Understanding of software design principles, API development, microservices, asynchronous processing, and distributed systems.
Preferred
•Experience with additional programming languages used in enterprise, cloud-native, data, or AI application development.
•Experience developing reusable AI libraries, shared services, architecture accelerators, and internal platforms.
•Familiarity with event-driven and distributed application development.
Databases/Data Management
Essential
•Strong understanding of data engineering concepts and enterprise data architecture.
•Experience with data lakes, structured and unstructured data, data ingestion, data preparation, and distributed processing.
•Understanding of data quality, metadata, lineage, access control, retention, and governance.
•Experience with vector databases, embeddings, semantic search, retrieval, and knowledge bases.
•Ability to design data architectures that support model training, inference, RAG, evaluation, and monitoring.
•Understanding of data privacy, protection, classification, and responsible data usage.
Preferred
•Exposure to Databricks, Snowflake, or comparable data platforms.
•Experience with feature stores, vector search platforms, data warehouses, and analytical data models.
•Familiarity with data mesh, data fabric, semantic models, and enterprise knowledge-management patterns.
•Experience with data-quality automation, lineage tracking, and data-governance frameworks.
Cloud Technologies
Essential
•Experience designing and implementing AI solutions on Azure, AWS, or Google Cloud Platform.
•Understanding of cloud-native architecture, compute, storage, networking, identity, security, monitoring, and integration services.
•Experience designing scalable, highly available, secure, and maintainable AI platforms.
•Understanding of cloud deployment, environment management, CI/CD, infrastructure automation, and operational support.
•Ability to evaluate cloud AI services and select appropriate services based on business, technical, security, and cost requirements.
Preferred
•Exposure to Azure AI Foundry and Azure Machine Learning.
•Experience with cloud-based data platforms, model-serving infrastructure, serverless services, and containerized deployments.
•Familiarity with Kubernetes, Docker, infrastructure as code, and cloud platform engineering.
•Understanding of cloud cost optimization and sustainable AI workload design.
Frameworks and Libraries
Essential
•TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain, LlamaIndex, or equivalent AI/ML frameworks.
•Large Language Models, including OpenAI, Azure OpenAI, Claude, Gemini, or open-source models.
•RAG architectures, embeddings, vector databases, semantic search, retrieval, reranking, and evaluation frameworks.
•AI orchestration frameworks supporting workflows, tools, agents, model integration, and application coordination.
•Frameworks supporting MLOps, LLMOps, model lifecycle management, monitoring, evaluation, and observability.
•API and microservices frameworks for enterprise AI application development.
Preferred
•Agentic AI frameworks and multi-agent architectures.
•Conversational AI and knowledge-management frameworks.
•Model-serving, prompt-management, evaluation, and governance frameworks.
•Fine-tuning and adaptation frameworks for specialized models and domain-specific use cases.
•Frameworks supporting explainability, fairness, safety testing, and responsible AI validation.
Development Tools and Methodologies
Essential
•Experience across the AI solution lifecycle, including business analysis, architecture, design, development, testing, deployment, monitoring, support, and continuous improvement.
•Experience defining technical approaches, reference architectures, delivery plans, quality standards, and governance controls.
•Expertise in MLOps and LLMOps practices, including model lifecycle management, CI/CD, deployment automation, experiment tracking, monitoring, evaluation, and observability.
•Experience with architecture reviews, solution assessments, design reviews, technical evaluations, and risk analysis.
•Familiarity with Agile delivery, iterative development, backlog planning, technical estimation, and release management.
•Ability to create architecture documents, technical standards, design patterns, decision records, operational procedures, and solution presentations.
•Experience mentoring engineers and promoting consistent engineering practices.
Preferred
•Experience with infrastructure as code, containers, Kubernetes, and automated deployment.
•Familiarity with model registries, feature stores, experiment tracking, automated regression evaluation, and drift monitoring.
•Experience designing reusable platform capabilities, AI accelerators, and internal developer services.
•Experience supporting enterprise-scale AI transformation programs and proposal development.
Security Protocols
Essential
•Strong understanding of AI security, governance, privacy, compliance, explainability, and ethical AI practices.
•Ability to apply identity and access controls, least-privilege permissions, secrets management, encryption, secure APIs, and protected data flows.
•Understanding of risks related to prompt injection, data leakage, unauthorized model or tool access, unsafe outputs, model misuse, and insecure integrations.
•Ability to define security and governance controls for training data, prompts, models, vector databases, APIs, applications, and operational environments.
•Experience incorporating responsible AI principles into solution architecture, development, evaluation, deployment, and monitoring.
•Understanding of auditability, traceability, model documentation, risk assessment, and human oversight.
Preferred
•Experience with AI threat modeling, privacy-preserving AI, model security, adversarial testing, and compliance automation.
•Familiarity with model cards, data sheets, AI impact assessments, fairness testing, and explainability techniques.
•Experience establishing governance frameworks for enterprise AI and Generative AI adoption.
•Knowledge of security validation within CI/CD, MLOps, LLMOps, and cloud deployment workflows.
Experience Requirements
•Overall experience: 11 years of professional IT experience.
•Architecture experience: Strong experience in AI/ML solution architecture, enterprise architect