
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.

About AbleCredit
About
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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Location - Remote
Stipend - 5K - 10K
bout the Role
We are seeking an experienced Python Data Engineer with a strong foundation in API and basic UI development. This role is essential for advancing our analytics capabilities for AI products, helping us gain deeper insights into product performance and driving data-backed improvements. If you have a background in AI/ML, familiarity with large language models (LLMs), and a solid grasp of Python libraries for AI, we’d like to connect!
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• Data Analysis for Actionable Insights: Dive into complex data sets to identify patterns and provide actionable insights to support product improvements.
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• Clear Reporting of Findings: Present findings to stakeholders in a clear, concise manner, emphasizing actionable insights.
Required Skills
• Technical Skills:
o Proficiency in Python, including experience with key AI/ML libraries.
o Basic knowledge of UI and API development.
o Understanding of large language models (LLMs) and experience using them effectively.
• Analytical & Communication Skills:
o Strong problem-solving skills to address complex, ambiguous challenges.
o Ability to translate data insights into understandable reports for non-technical stakeholders.
o Knowledge of machine learning algorithms and frameworks to assess AI product effectiveness.
o Experience in statistical methods to interpret data and build metrics frameworks.
o Skilled in quantitative analysis to drive actionable insights.












