Overview

Data, Cloud & AI Engineer

Responsibilities

• Own S3 data-quality monitoring by emitting custom CloudWatch metrics and alarms, improving anomaly detection and protecting downstream reporting and product decisions.
• Build semantic data models, fine-tune dbt transformations for high-volume real-time events, and implement Airbyte reverse-ETL pipelines that make trusted metrics available across the organisation.
• Optimise Amazon Redshift queries for performance and cost, maintain responsive Metabase or comparable dashboards, and define KPIs that measure recommendation-engine and data-product impact.
• Design and operate highly available, scalable AWS and Azure infrastructure, applying Terraform, Ansible and FinOps controls to improve resilience, security and cloud economics.
• Automate CI/CD through Git or Bitbucket, GitLab CI and Jenkins, embedding unit, integration and static-analysis checks that raise release quality and reduce deployment risk.
• Deploy microservices, databases and front-end applications on Kubernetes, with Prometheus, Grafana, ELK and CloudWatch monitoring APIs, networks, databases and user interfaces.
• Build production LLM features using RAG, embeddings, vector stores, agentic workflows and model APIs, while owning versioning, evaluation, guardrails, auditability and inference operations.
• Strong Python, advanced SQL and Bash skills, with production experience in data engineering, cloud or DevOps, and ML engineering across at least two areas.
• Hands-on expertise with dbt, Amazon Redshift, S3, Airbyte, streaming or real-time data, semantic modelling, atomic data tests and Metabase or comparable BI tools.
• Practical AWS capability across core services, IAM, CloudWatch and Bedrock, plus working Azure knowledge; Docker, Kubernetes, Terraform, Ansible, GitLab CI, Jenkins, Prometheus, Grafana and ELK.
• Experience with LLM APIs, RAG, embeddings, vector databases, prompt engineering, model evaluation, deployment monitoring and Responsible AI controls such as PII redaction and prompt-injection defence.
• Ability to integrate foundation models including Anthropic Claude, OpenAI, Azure OpenAI, AWS Bedrock or open-source alternatives, selecting approaches against quality, latency, security and cost requirements.
• Understanding of MLOps or LLMOps, including model and prompt versioning, inference monitoring, token-cost tracking, evaluation harnesses, groundedness, hallucination, drift and output filtering.
• Clear written and verbal communication, strong problem-solving ability, secure engineering habits, effective code reviews and comfort owning systems end to end.
• AWS, Azure, CKA or Terraform certifications, along with Kafka, Kinesis or Spark Streaming experience, can strengthen your ability to operate scalable production platforms.
• Experience fine-tuning or serving open-source models with Hugging Face, vLLM or Ollama can accelerate model experimentation and deployment flexibility.
• Familiarity with Airflow, Dagster or Temporal can improve orchestration of data, model and document-processing workflows.
• Knowledge of ISO/IEC 42001, NIST AI RMF, the EU AI Act, GRC or cybersecurity domains, recommendation systems or A/B testing is valuable.

Qualifications

• About the Company
• About the Role
• You bring a Bachelor's degree in Computer Science, Engineering or a related field, or equivalent practical experience, and two to five years of hands-on production engineering across at least two of data engineering, cloud/DevOps and ML engineering. You can work deeply in Python, advanced SQL and Bash while understanding how data models, infrastructure, applications and AI services behave in production.
• You have built or operated reliable pipelines, cloud environments, CI/CD systems, containers, Kubernetes workloads or model-enabled features, and you can explain the operational and business impact of your decisions. You are comfortable working with AWS and developing working knowledge of Azure, applying security, observability, FinOps and testing rather than treating them as afterthoughts.
• You can evaluate LLM systems pragmatically, protect sensitive data, document trade-offs and collaborate with stakeholders who may not share your technical background. The team needs someone curious, accountable and resourceful: an engineer who can move from investigation to implementation, measure whether a system works, and improve it continuously in a lean environment.
• Own data, cloud and AI systems end to end at an early-stage deep tech company, with the autonomy to shape architecture, engineering standards and operational outcomes.
• Build Responsible AI products used by enterprise clients in third-party risk, GRC and cybersecurity compliance, turning advanced technology into practical trust and governance capabilities.
• Work directly in a lean team where your decisions influence platform reliability, product direction and the quality of systems customers rely on.
• Expand your depth across data engineering, multi-cloud infrastructure, DevOps and AI engineering while learning how to operate technology responsibly in regulated contexts.

Benefits

• Full-time, on-site work five days per week from collaborative offices in Andheri or Thane, with direct engagement across engineering, product and delivery priorities.
• End-to-end ownership across data, cloud, DevOps and AI systems, enabling meaningful technical scope and visible impact on enterprise-facing Responsible AI products.
• Learning budget and support for professional certifications, including opportunities aligned with cloud, infrastructure, security and Responsible AI development.
• A collaborative environment at a Mumbai-based deep tech company where a lean team can see its work directly shape the platform and customer outcomes.