Technical Solution Architect - AI & ML Products

Description : Role Summary : We are hiring a Tech Solution Architect to design end-to-end technology solutions for a portfolio of strategic AI and data products. This role will own solution architecture, system design, and technology choices across products, and work closely with dedicated specialists (Data Engineering, Data Architecture, MLOps, DevOps, Cloud, Security, AI Engineering) to drive production-ready delivery. The focus is on architectural clarity, integration design, non-functional requirements, and technical decision-making that accelerates product teams while ensuring scalability, reliability, security, and governance.Key Responsibilities : - Own end-to-end solution architecture (HLD/LLD) for multiple AI/data products: workflows, data flows, service boundaries, integrations, and NFRs.- Define and communicate reference architectures for product capabilities spanning data ingestion/serving, ML/GenAI components, APIs, and orchestration.- Drive key technology and design decisions (stack selection, build vs buy, batch vs real-time, integration patterns), documenting rationale and tradeoffs.- Design integration architecture with enterprise systems and data sources: API strategy, eventing patterns, identity/SSO, authorization, logging, and audit trails.- Partner with Data Architects/Data Engineers to define data contracts, access patterns, data quality expectations, and governance requirements.- Partner with MLOps/AI Engineering to define productionization requirements for ML/GenAI (deployment, monitoring, evaluation, safety/guardrails).- Partner with DevOps/Cloud teams to define deployment architecture (environments, scalability, resiliency, observability, cost controls).- Collaborate with Security/Compliance to ensure privacy-by-design, secure architecture, and auditability- Lead technical design reviews and ensure architectural coherence across teams and vendors.Must have Skill Sets : 1) Solution Architecture & System Design (Core)- Strong experience designing distributed, scalable systems and data-heavy applications.- Ability to produce clear HLD/LLD: service decomposition, data flows, sequence diagrams, API specs, NFRs.- Strong integration design capability (REST/event-driven patterns, error handling, idempotency, versioning).2) Cross-Functional Technical Leadership- Proven ability to lead architecture across multiple specialist teams (data, cloud, DevOps, MLOps, security) without owning all execution.- Strong stakeholder management: can translate business outcomes into technical approaches and align diverse teams.3) Data & Analytics Architecture Literacy- Solid understanding of modern data architectures (warehouse/lake/lakehouse concepts), data modeling basics, and analytics access patterns.- Ability to define requirements for data quality, lineage, governance, and access controls in partnership with data specialists.4) ML Production & GenAI System Design Literacy- Strong understanding of what it takes to run ML systems in production: evaluation, deployment patterns, monitoring/drift concepts, retraining triggers.- For GenAI use cases: working knowledge of RAG architectures, prompt/versioning concepts, guardrails/safety patterns, and evaluation approaches.- Ability to define non-functional and governance requirements for AI systems (privacy, audit logs, safe usage, risk controls).5) Cloud-Native & Operational Readiness Literacy- Strong understanding of cloud architecture concepts: networking basics, IAM, encryption, secrets, scaling, resiliency.- Ability to specify requirements for CI/CD, observability (logs/metrics/traces), SLOs, and cost/performance constraints (execution owned by platform teams).6) Decision-Making & Documentation- Experience making and documenting architectural tradeoffs (build vs buy, latency vs cost, centralized vs decentralized).- Strong documentation discipline: design notes, architecture review artifacts, and decision records.Good to have Skill Sets : - Hands-on experience implementing MLOps/LLMOps/DevOps toolchains (e.g., MLflow, feature stores, Kubernetes, Terraform, CI pipelines).- Experience with data platforms/tools (Databricks/Spark, Snowflake/BigQuery, Kafka), vector databases, or LLM orchestration frameworks.- Domain exposure in CPG/FMCG, marketing analytics/media planning, pricing, forecasting/supply chain systems.Qualifications : - Bachelors/Masters in Computer Science, Engineering, or related field (or equivalent practical experience).- 612 years in roles such as Solution Architect, Technical Architect, Staff Engineer, Tech Lead, or Platform Engineer with demonstrable architecture ownership. (ref: hirist.tech)