AbleCredit
https://ablecredit.comAbout
AI that writes Credit Reports on its own !!
AbleCredit is a friendly, supportive credit assistant. It generates Credit Appraisal Memos based on your policies, without any human intervention.
Candid answers by the company
AI that writes Credit Reports on its own !!
AbleCredit is your friendly, supportive credit assistant. It generates Credit Appraisal Memos based on your policies, without any human intervention.
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Jobs at AbleCredit
SDE 2 / SDE 3 – AI Infrastructure & LLM Systems Engineer
Location: Pune / Bangalore (India)
Experience: 4–8 years
Compensation: no bar for the right candidate
Bonus: Up to 10% of base
About the Company
AbleCredit builds production-grade AI systems for BFSI enterprises, reducing OPEX by up to 70% across onboarding, credit, collections, and claims.
We run our own LLMs on GPUs, operate high-concurrency inference systems, and build AI workflows that must scale reliably under real enterprise traffic.
Role Summary (What We’re Really Hiring For)
We are looking for a strong backend / systems engineer who can:
- Deploy AI models on GPUs
- Expose them via APIs
- Scale inference under high parallel load using async systems and queues
This is not a prompt-engineering or UI-AI role.
Core Responsibilities
- Deploy and operate LLMs on GPU infrastructure (cloud or on-prem).
- Run inference servers such as vLLM / TGI / SGLang / Triton or equivalents.
- Build FastAPI / gRPC APIs on top of AI models.
- Design async, queue-based execution for AI workflows (fan-out, retries, backpressure).
- Plan and reason about capacity & scaling:
- GPU count vs RPS
- batching vs latency
- cost vs throughput
- Add observability around latency, GPU usage, queue depth, failures.
- Work closely with AI researchers to productionize models safely.
Must-Have Skills
- Strong backend engineering fundamentals (distributed systems, async workflows).
- Hands-on experience running GPU workloads in production.
- Proficiency in Python (Golang acceptable).
- Experience with Docker + Kubernetes (or equivalent).
- Practical knowledge of queues / workers (Redis, Kafka, SQS, Celery, Temporal, etc.).
- Ability to reason quantitatively about performance, reliability, and cost.
Strong Signals (Recruiter Screening Clues)
Look for candidates who have:
- Personally deployed models on GPUs
- Debugged GPU memory / latency / throughput issues
- Scaled compute-heavy backends under load
- Designed async systems instead of blocking APIs
Nice to Have
- Familiarity with LangChain / LlamaIndex (as infra layers, not just usage).
- Experience with vector DBs (Qdrant, Pinecone, Weaviate).
- Prior work on multi-tenant enterprise systems.
Not a Fit If
- Only experience is calling OpenAI / Anthropic APIs.
- Primarily a prompt engineer or frontend-focused AI dev.
- No hands-on ownership of infra, scaling, or production reliability.
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