AbleCredit

Role: Senior Security Engineer

Salary: INR 20-35L per annum

Performance Bonus: Up to 10% of the base salary

Location: Hulimavu, Bangalore, India

Experience: 5-8 years

About AbleCredit:

AbleCredit has built a foundational AI platform to help BFSI enterprises reduce OPEX by up to 70% by powering workflows for onboarding, claims, credit, and collections. Our GenAI model achieves over 95% accuracy in understanding Indian dialects and excels in financial analysis.

The company was founded in June 2023 by Utkarsh Apoorva (IIT Delhi, built Reshamandi, Guitarstreet, Edulabs); Harshad Saykhedkar (IITB, ex-AI Lead at Slack); and Ashwini Prabhu (IIML, co-founder of Mythiksha, ex-Product Head at Reshamandi, HandyTrain).

What Work You’ll Do

• Be the guardian of trust — every system you secure will protect millions of data interactions.
• Operate like a builder, not a gatekeeper — automate guardrails that make security invisible but ever-present.
• You’ll define what ‘secure by default’ means for a next-generation AI SaaS platform.
• Own the security posture of our cloud-native SaaS platform — design, implement, and enforce security controls across AWS, Linux, and Kubernetes (EKS) environments.
• Drive security compliance initiatives such as SOC 2 Type II, ISO 27001, and RBI-aligned frameworks — build systems that enforce, not just document, compliance.
• Architect defense-in-depth systems across EC2, S3, IAM, and VPC layers, ensuring secure configuration, least-privilege access, and continuous compliance.
• Build and automate security pipelines — integrate AWS Security Hub, GuardDuty, Inspector, WAF, and CloudTrail into continuous detection and response systems.
• Lead vulnerability management and incident readiness — identify, prioritize, and remediate vulnerabilities across the stack while ensuring traceable audit logs.
• Implement and maintain zero-trust and least-privilege access controls using AWS IAM, SSO, and modern secrets management tools like AWS SSM or Vault.
• Serve as a trusted advisor — train developers, review architecture, and proactively identify risks before they surface.

The Skills You Have..

• Deep hands-on experience with AWS security architecture — IAM, VPCs, EKS, EC2, S3, CloudTrail, Security Hub, WAF, GuardDuty, and Inspector.
• Strong background in Linux hardening, container security, and DevSecOps automation.
• Proficiency with infrastructure-as-code (Terraform, CloudFormation) and integrating security controls into provisioning.
• Knowledge of zero-trust frameworks, least-privilege IAM, and secrets management (Vault, SSM, KMS).
• Experience with SIEM and monitoring tools — configuring alerts, analyzing logs, and responding to incidents.
• Familiarity with compliance automation and continuous assurance — especially SOC 2, ISO 27001, or RBI frameworks.
• Understanding of secure software supply chains — dependency scanning, artifact signing, and policy enforcement in CI/CD.
• Ability to perform risk assessment, threat modeling, and architecture review collaboratively with engineering teams.

What You Should Have Done in the Past

• Secured cloud-native SaaS systems built entirely on AWS (EC2, EKS, S3, IAM, VPC).
• Led or contributed to SOC 2 Type II or ISO 27001 certification initiatives, ideally in a regulated industry such as FinTech.
• Designed secure CI/CD pipelines with integrated code scanning, image validation, and secrets rotation.
• (Bonus) Built internal security automation frameworks or tooling for continuous monitoring and compliance checks.

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.