Solutions Architect - AI/ML

Job overview Job Title: AI/ML Solution Architect Location: Mumbai Experience: 10–15 years (with strong AI/ML & cloud background) Reporting to: Head of AI/ML Function: Delivery – Customer Solutions Aivar is an AI‑first technology partner that helps startups, digital businesses, and enterprises move from AI experiments to production‑grade systems using AI‑augmented teams, reusable accelerators, and managed services. As an AI/ML Solution Architect, you will own end‑to‑end solution design for strategic customer engagements across AI, data, and cloud, ensuring we deliver scalable, secure, and production‑ready solutions on aggressive timelines. Role purpose This role bridges customer business problems with Aivar’s accelerators, platform capabilities, and engineering teams. You will lead discovery, shape architectures for AI/ML and GenAI solutions, and guide delivery squads to implement them using best‑practice MLOps/LLMOps, data, and cloud patterns. You are successful when customers see faster time‑to‑production, lower delivery risk, and clear, measurable business outcomes from the AI solutions you help design. Key responsibilities Lead technical discovery with customers to understand business goals, data landscape, constraints, and success metrics, and translate them into clear solution architectures. Design end‑to‑end AI/ML and GenAI solutions: data ingestion and transformation, feature engineering, model selection, training/evaluation, deployment, and monitoring. Leverage Aivar’s reusable accelerators (e.g., infra accelerators like Kubogent, RAG/analytics/copilot blueprints) to reduce risk and implementation time wherever possible. Define cloud architecture on AWS (and optionally Azure/GCP): VPC, networking, security, storage, compute, and CI/CD tailored for AI and data workloads. Own non‑functional requirements: reliability, performance, cost, observability, disaster recovery, and security for AI workloads. Partner with Delivery Leads and Engineering Managers to break architectures into epics and stories, ensuring the implementation aligns with the blueprint. Provide hands‑on guidance and technical leadership to AI/ML engineers, data engineers, backend engineers, and DevOps/SRE teams during delivery. Establish and enforce best practices for MLOps/LLMOps: experiment tracking, model registry, automated tests, CI/CD for models, monitoring, and rollback strategies. Participate in pre‑sales and solutioning: estimate effort, shape SOWs, present architectures to customers, and support proposal/bid responses. Act as a trusted advisor to customer architects and CTOs, presenting trade‑offs and guiding technology choices across AI, data, and cloud. Continuously feed learning from projects back into Aivar’s accelerators, reference architectures, and internal playbooks. Required qualifications 10+ years of experience in software / data / AI engineering, including at least 3–5 years architecting AI/ML or advanced analytics solutions on cloud. Strong hands‑on experience with ML and/or GenAI: supervised/unsupervised learning, NLP, recommendation, or LLM‑based applications, preferably in production environments. Solid understanding of modern data platforms: data lakes/warehouses, batch and streaming pipelines, data quality, and governance. Deep experience with at least one major cloud platform, preferably AWS (VPC, IAM, ECS/EKS, Lambda, S3, RDS/NoSQL, networking, monitoring, etc.) Familiarity with MLOps tools and patterns (e.g., SageMaker/Vertex/Azure ML, MLflow, Kubeflow, feature stores, model registry, CI/CD for ML). Strong architecture skills: ability to design distributed systems, API‑first solutions, and integration patterns that fit into complex customer environments. Proficiency in Python for ML/AI work; exposure to other languages (e.g., Java/Scala/TypeScript) is a plus. Excellent communication and stakeholder‑management skills; comfortable driving workshops, writing solution documents, and presenting to senior customer stakeholders. Preferred qualifications Experience in GenAI/LLM scenarios such as copilots, RAG‑based knowledge assistants, document automation, or conversational AI. Prior work in services / consulting, especially with productised service offerings and accelerators. Domain experience in one or more of Aivar’s focus segments (e.g., SaaS, fintech, e‑commerce, manufacturing, or enterprise IT). Familiarity with DevOps/SRE practices and tools: infrastructure as code (Terraform/CloudFormation), GitOps, observability stacks, SLOs/SLIs. Exposure to security and compliance requirements relevant to AI workloads (PII handling, data residency, access controls, auditability).