Senior AI & Data Engineer India

Title: Senior AI & Data Engineer
Location: India only | Remote Full time Permanent
Type: Full-time
Core responsibilities & objectives
Design, build, and maintain batch/streaming data pipelines, ingestion, cleaning, normalisation, enrichment, deduplication.
Build and own ML/LLM pipelines end-to-end: document and log parsing, chunking, embeddings generation, vector indexing, agentic tool calling, multi-step workflows, retries, fallbacks, and state handling.
Turn raw execution logs and documents into reliable, versioned training and evaluation datasets, with leakage-aware splits and measurable quality gates.
Build reproducible evaluation harnesses: measure tool-selection accuracy, hallucination rates, and failure slices — not just aggregate metrics.
Write production-grade, well-tested Python that processes large volumes of data and documents reliably.
Own pipeline health: if data is stale, broken, or wrong, it's on you.
Work autonomously to project deadlines with minimal hand-holding.
Key qualifications & skills (non-negotiable)
7+ years in backend data-heavy development or data engineering
Previously worked in Startup
Highly proficient in Python
Hands-on experience with large datasets and high-velocity data streams (Kafka, Flink, Spark).
Strong with pipeline orchestration tools (Airflow, MLflow, or equivalent).
Solid SQL skills (Postgres, BigQuery, or Snowflake) and NoSQL experience (DynamoDB, OpenSearch, Elastic).
Real experience with LLM workflows: RAG architectures, embeddings/vector DBs, prompt engineering, function/tool calling, observability.
Hands-on LLM dataset preparation and evaluation: deduplication, sampling, train/validation splitting without task or session leakage, and tracing bad metrics back to inputs and labels.
Applied experience with supervised fine-tuning or model benchmarking, and an understanding of quality-vs-cost trade-offs.
Deep understanding of ETL/ELT patterns and data processing at scale.
Preferred background (strong signals)
Experience with AWS data stack at scale (S3, Glue, EC2/GPU instances).
Exposure to enterprise or regulated environments where data governance matters.
Built and shipped data, ML and LLM-powered pipelines in production.
Experience with knowledge distillation, parameter-efficient fine-tuning (LoRA/QLoRA), or long-context evaluation.
Familiarity with model serving concerns (vLLM, quantisation, latency/cost trade-offs), even if you haven't owned inference infrastructure.
Has debugged a pipeline and knows why observability matters.
Worked in a fast-moving startup where "that's not my job" doesn't exist.
What will get you rejected
"I set up the pipeline, someone else monitors it" mindset.
Tutorials and side projects but no production experience at scale.
Prompt-only AI experience — you've never built or evaluated the data and training pipeline behind the model.
Can't explain trade-offs between streaming vs. batch, why you chose one vector DB over another, or how you split training data to avoid leakage.
Needs detailed specs before writing a line of code.
No curiosity about what the data actually means or how the model uses it.
Interested? We're a distributed team solving hard problems in enterprise AI — turning messy logs and documents into models that reliably automate complex tool-use workflows. If you want ownership, not just tickets, we'd like to hear from you.

Work arrangement
No