AI/ML Solution Architect

New

Remote - India, Flexibility to work across timezones; early mornings or late evenings can be a regular part of the rhythmFull-TimeSenior

Salary not disclosed

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Job Details

Languages: Strong written English
Experience: 8–13 years (strong 6–8 considered if you have shipped production GenAI)
Required Skills: AWSPythonAzureMLOpsGenerative AI

Have 8+ years building software or data/AI systems, with recent experience as a Solution Architect, AI/ML Architect, or Principal/Staff Engineer owning end-to-end architecture.
Have shipped at least one production AI/ML system, such as RAG, multi-agent, LLM-as-judge, or a comparable classical ML/NLP pipeline at scale.
Produce delivery-ready architecture blueprints covering services, data stores, IAM, failure modes, evaluation strategy, and cost models.
Bring deep Azure and/or AWS cloud experience, including landing-zone and private-endpoint architecture.
Be fluent in Python, with hands-on experience in classical ML, model training, evaluation, feature pipelines, LLM APIs, embeddings, and vector search.
Run workshops with non-engineers and translate requirements between domain specialists, IT stakeholders, monitoring/evaluation teams, and engineering.
Have strong written English for documentation that functions as a binding technical contract.
Preferred: Azure AI and experience with AWS or GCP services such as Bedrock, SageMaker, Vertex, or BigQuery.
Preferred: LLMOps/MLOps, prompt and model versioning, MLflow or equivalent, evaluation harnesses, and CI release gates.
Preferred: Document AI/OCR at volume, RAG over structured knowledge, hybrid search, and experience in education, assessment, public sector, or other high-audit domains.
Preferred: agent security, FinOps, relevant Azure or AWS certifications, and sovereign-hosting or government data-system experience.

Lead solution workshops with programme, ministry, and engineering stakeholders.
Translate requirements into service designs, data stores, sequence diagrams, decision gates, and architecture decision records.
Architect generation, retrieval, judging, evaluation, and constraint-based paper assembly pipelines.
Design human-and-AI workflows with audit controls, segregation of duties, and protection of student PII.
Govern models, prompts, evaluation gates, fallbacks, and OCR provider routing.
Shape Azure and/or AWS platforms, including identity, networking, LLMOps/MLOps, observability, and sovereign hosting.
Define FinOps controls, cost caps, model routing, and degradation behavior.
Stay accountable through production delivery and mentor technical leads on model and workflow boundaries.

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