AI Platform Engineer

US/India Deep-Tech Startup | Chennai & San Jose (Full-time • On-site / Hybrid) We are a US/India deep-tech startup based in San Jose, CA and Chennai , building next-generation AI-driven medical intelligence systems . real-time data systems, safety-critical engineering, applied machine learning, and We operate in a domain where reliability, interpretability, and intelligent automation truly matter. We are looking for an engineer who can design end-to-end AI and data platforms —from data ingestion to inference—across vector search, graph-based modeling, ML infrastructure, and cloud-native deployment. This role blends modern ML Ops, data engineering, knowledge retrieval, and systems design. Help design hybrid retrieval workflows (vector + graph + rules). ML & Inference Infrastructure Set up and manage ML pipelines , model serving endpoints, and inference layers. Work on streaming/batch data flows across AWS/GCP. Build observability and monitoring for data quality, drift, and safety. Cloud & Infra Automation Deploy cloud resources using AWS + Terraform (EC2, S3, RDS/Neptune, networking, IAM). Manage containerized services (Docker + optional Kubernetes experience). Work cross-discipline to design architectures that integrate real-time data, ML models, graph semantics, and domain constraints. Bring strong reasoning on system trade-offs, reliability, safety, and performance. Experience with vector databases (pgvector, Pinecone, Weaviate, Milvus) or strong understanding of semantic search. Experience with graph databases or knowledge representation (Neo4j, Neptune, RDF, SPARQL, property graphs). Solid grounding in Python , data pipelines, and model-serving frameworks. Hands-on experience with AWS services (EC2, S3, IAM, networking). Bonus Skills (not required but appreciated) Experience building or maintaining RAG pipelines. Understanding of ML observability and safety. Interest in temporal reasoning, anomaly detection, or multimodal data. You want to work on ML infrastructure that actually matters in the real world. Chennai preferred, hybrid options available. Qualifications & Experience Required Qualifications We are looking for engineers with strong foundational experience across data systems, ML infrastructure, or cloud-native platforms. Solid hands-on experience with Python and at least one ML/DS workflow. Working knowledge of cloud services (AWS preferred; Experience setting up or maintaining data pipelines (batch or streaming). Good understanding of databases — SQL, NoSQL, or graph. Experience deploying or managing infra using Terraform or other IaC frameworks. Preferred Experience (Good to Have, Not Mandatory) These are not hard requirements — but if you have them, we’d love to hear about it: Experience with graph databases (Neo4j, Neptune, RDF, SPARQL, ArangoDB). Hands-on with Docker , containerized ML workloads, or microservices. Exposure to ML Ops tools (SageMaker, Vertex, MLFlow, Kubeflow, BentoML). Understanding of vector DBs like Pinecone, Weaviate, Milvus, or pgvector. Knowledge of data modeling , schema design, or graph-based reasoning. Experience building systems that require safety, accuracy, and auditability . Interest in healthcare, IoT, or real-time monitoring systems. Experience Range We expect candidates to have a meaningful track record building or deploying technical systems. Typically this means 4–10 years of experience , depending on depth and exposure. You will work on deeply meaningful problems at the frontier of AI, safety, and intelligent automation. This is not a typical “ML model training” role — it’s a platform engineering role for someone who likes to architect, design, and build real systems end-to-end.