Data, Cloud & AI Engineer

Job Description — Data, Cloud & AI Engineer
Oct 7, 2026 · @Aakanksha Joshi

Role overview

We are hiring a hands-on engineer to own our data platform, cloud infrastructure and AI/LLM systems end to end. You will build reliable pipelines, keep our AWS and Azure environments secure and cost-efficient, and ship production-grade AI features into our products.
About Whyminds: Whyminds is a Mumbai-based Responsible AI and deep tech company specialising in third-party risk management (TPRM), GRC automation and cybersecurity compliance. As part of a lean team, your work will directly shape the platform our clients rely on.

Key responsibilities

Data engineering
Monitor data quality of objects stored in S3; emit custom AWS CloudWatch metrics and set up alarms for anomalies.
Implement a semantic data modelling layer that centralises the metrics used across the organisation.
Build and fine-tune dbt models processing real-time, high-volume event data (e.g., advertisement impressions).
Implement reverse-ETL pipelines using Airbyte.
Position Data, Cloud & AI Engineer
Company Whyminds Global Solutions Pvt. Ltd. (RegAhead)
Location WeWork BKC, Mumbai
Experience 3–5 years
Employment type Full-time
Job Description — Data, Cloud & AI Engineer

Optimise Amazon Redshift queries for performance and cost.
Build and maintain responsive dashboards and visualisations in Metabase or a chosen framework.
Nurture data quality through atomic, data-oriented tests.
Define KPIs measuring the impact of recommendation engines and other data products.
Cloud infrastructure & DevOps
Design, manage and optimise high-availability, scalable and cost-effective infrastructure on AWS and Azure.
Automate CI/CD with Git/Bitbucket, GitLab CI and Jenkins; embed unit, integration and static code analysis checks in pipelines.
Provision and configure infrastructure as code using Terraform and Ansible.
Deploy and manage microservices, databases and front-end applications on Kubernetes.
Set up monitoring, logging and alerting for APIs, databases, network and UIs using
Prometheus, Grafana, ELK and CloudWatch.
Adopt open-source frameworks for authentication, authorisation, workflows, document and service management to reduce managed-service spend.
Apply FinOps practices to track and reduce cloud costs without compromising performance.
AI engineering
Build and deploy LLM-powered features, including retrieval-augmented generation (RAG) pipelines over compliance and risk documents.
Design and maintain embedding pipelines and vector stores (e.g., pgvector, OpenSearch, Pinecone, Qdrant).
Develop agentic workflows and tool-calling integrations using frameworks such as LangChain, LlamaIndex or native model SDKs.
Integrate and evaluate foundation models via APIs (Anthropic Claude, OpenAI, Azure
OpenAI, AWS Bedrock) and open-source models.
Own MLOps/LLMOps: model and prompt versioning, deployment, inference monitoring, latency and token-cost tracking.
Job Description — Data, Cloud & AI Engineer

Build evaluation harnesses to measure accuracy, hallucination rate, groundedness and drift.
Implement Responsible AI guardrails — PII redaction, prompt-injection defences, output filtering and audit logging — aligned with Whyminds' governance standards.
Prepare and curate datasets for fine-tuning, classification and document extraction (OCR, entity extraction).
Required skills and qualifications
Bachelor's degree in Computer Science, Engineering or a related field (or equivalent experience).
3–5 years across data engineering, cloud/DevOps or ML engineering, with hands-on production experience in at least two of the three.
Strong problem-solving skills and comfort owning systems end to end in a small, fast-moving team.
Clear written and verbal communication with technical and non-technical stakeholders.
Area Must-have skills
Languages Python, advanced SQL, Bash
Data dbt, Amazon Redshift, S3, Airbyte, streaming/real-time data, data modelling, data testing
BI Metabase or similar (Superset, Looker, Power BI)
Cloud AWS (core services, CloudWatch, IAM, Bedrock);
working knowledge of Azure
DevOps & IaC Docker, Kubernetes, Terraform, Ansible, GitLab CI, Jenkins, Git/Bitbucket
Observability Prometheus, Grafana, ELK, CloudWatch
AI / LLM LLM APIs, RAG, embeddings and vector databases, prompt engineering, LLM evaluation
Practices FinOps, security best practices, code reviews, documentation
Job Description — Data, Cloud & AI Engineer

Good to have

AWS, Azure, CKA or Terraform certifications.
Experience with Kafka, Kinesis or Spark Streaming.
Fine-tuning or serving open-source models (Hugging Face, vLLM, Ollama).
Experience with Airflow, Dagster or Temporal for orchestration.
Familiarity with AI governance frameworks (ISO/IEC 42001, NIST AI RMF, EU AI Act) or GRC/cybersecurity domains.
Experience building recommendation systems or A/B testing frameworks.

What we offer

End-to-end ownership of data, cloud and AI systems at an early-stage deep tech company.
Direct impact on Responsible AI products used by enterprise clients.
Learning budget and support for certifications.
Collaborative workspace at WeWork BKC, Mumbai.

How to apply

Send your CV and a short note on a data, cloud or AI system you have built to the hiring contact.
Confirm the careers email, compensation range and work-mode (on-site/hybrid) before publishing.
Job Description — Data, Cloud & AI Engineer