We’re hiring an ML Engineer (~1 year experience) to own LLM orchestration, latency, and scaling for workflows already live with customers. Available to join immediately or within 1 month

This role is production ML systems engineering , not model training.

What You’ll Do

• Build and operate end-to-end LLM pipelines for full-document analysis (100–500+ page contracts)
• Implement schema-first LLM inference using Pydantic to produce deterministic, typed outputs
• Own LLM orchestration logic : prompt routing, validation, retries, fallbacks, and partial re-execution
• Optimize latency, throughput, and cost for long-context inference (batching, streaming, async execution)
• Build and scale OCR → document parsing → LLM inference pipelines for scanned leases (Textract)
• Develop streaming and async APIs using FastAPI
• Manage distributed background workloads with Celery (queues, retries, idempotency, backpressure)
• Productionize report generation (DOCX/EXCEL) as deterministic pipeline outputs
• Deploy, monitor, and scale inference workloads on AWS (Bedrock, EC2, S3, Lambda)
• Debug production issues: timeouts, schema failures, partial extractions, cost spikes

What You’ll Own Technically

• Pydantic-based schemas for all LLM outputs
• Prompt ↔ schema contracts and versioning
• Validation, retry, and fallback mechanisms
• Latency and cost optimization for long-context inference
• Reliability of OCR + LLM pipelines at scale

Must Have

• Strong Python and async programming fundamentals
• ~1 year experience working on production ML or LLM systems
• Hands-on experience with Claude, Gemini , and AWS Bedrock
• Experience with schema-constrained LLM outputs (Pydantic, JSON Schema, or similar)
• Experience with OCR and document-heavy pipelines
• Experience with Celery or distributed async job systems
• Comfort treating LLMs as non-deterministic services requiring validation and retries
• Individual contributor mindset in a lean startup
• Available to join immediately or within 1 month

Nice to Have (Strong ML Signals)

• Experience with streaming LLM responses
• Familiarity with long-context failure modes and truncation issues
• Experience with LLM output evaluation or regression testing
• Cost monitoring and optimization for LLM inference

Why Join Us

• Work on real production ML systems , not demos
• Own core LLM infrastructure end-to-end
• Direct exposure to long-context, document-scale AI
• Fully remote, fast-paced startup
• CTC: ₹9,00,000 – ₹12,00,000 (based on experience & impact)