AI Platform Engineer

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

(Full-time
• On-site / Hybrid)

About Us

We are a US/India deep-tech startup based in
San Jose, CA
and
Chennai
, building next-generation
AI-driven medical intelligence systems
.

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

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.

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

We don't expect you to know everything — but you should be strong in
some
of these areas and curious about the rest:

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

We are looking for engineers with
strong foundational experience
across data systems, ML infrastructure, or cloud-native platforms. You should have:

• 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

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.

Why Join Us

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.