AI Platform Engineer / ML Infrastructure Engineer

US/India Deep-Tech Startup | Chennai & San Jose

(Full-time • On-site / Hybrid)

About Us

San Jose, CA

Our work sits at the intersection of:

• real-time data systems,
• safety-critical engineering,
• applied machine learning, and
• modern information retrieval (vector search, graph reasoning, RAG).

We operate in a domain where reliability, interpretability, and intelligent automation truly matter.

If you enjoy working on technically challenging problems that have real-world impact, you’ll feel at home here.

Role Overview

end-to-end AI and data platforms

This role blends modern ML Ops, data engineering, knowledge retrieval, and systems design.

If you are comfortable thinking across multiple layers of the stack and enjoy solving open-ended problems, we’d love to speak with you.

What You’ll Work OnData & Retrieval Systems

• Build and optimize pipelines using

  vector search

  , embeddings, and metadata-aware retrieval.
• Experiment with

  graph / knowledge-based systems

  (e.g., RDF stores, property graphs, knowledge graphs).
• 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).
• Ensure scalable, secure, and reproducible infrastructure.

Systems-Level Problem Solving

• 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.

What We’re Looking For

some

Core Skills

• 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).
• Practical understanding of

  Terraform

  or infra-as-code.

Bonus Skills (not required but appreciated)

• Experience building or maintaining RAG pipelines.
• Understanding of ML observability and safety.
• Exposure to healthcare or high-reliability systems.
• Interest in temporal reasoning, anomaly detection, or multimodal data.

Who You Are

• You think like a systems engineer—holistic, curious, rigorous.
• You enjoy technical depth more than buzzwords.
• You are comfortable exploring ambiguous problems.
• You want to work on ML infrastructure that actually matters in the real world.
• You appreciate startups where engineers have autonomy and ownership.

Location

Chennai preferred, hybrid options available.

Qualifications & Experience

Required Qualifications

strong foundational experience

• Solid hands-on experience with

  Python

  and at least one ML/DS workflow.
• Working knowledge of

  cloud services

  (AWS preferred; GCP/Azure okay).
• Experience setting up or maintaining

  data pipelines

  (batch or streaming).
• Exposure to

  vector search

  , embeddings, or semantic retrieval systems.
• Good understanding of

  databases

  — SQL, NoSQL, or graph.
• Experience deploying or managing infra using

  Terraform

  or other IaC frameworks.
• Ability to design and reason about

  systems

  (latency, scaling, reliability, security).
• Strong debugging and problem-solving mindset.

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).
• Familiarity with

  RAG pipelines

  , retrieval frameworks, or knowledge graphs.
• 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

meaningful track record

4–10 years of experience

Why Join Us

You will work on deeply meaningful problems at the frontier of AI, safety, and intelligent automation.

platform engineering