Bjak is a technology company focused on making financial services easy, fun and more rewarding for everyone
Applied AI Engineer
Location
India
Posted
5 hours ago
Salary
0
Seniority
Senior
Job Description
Applied AI Engineer
BJAK
• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.
Job Requirements
- Hands-on engineering ability, not just AI strategy or management experience.
- Experience building AI agents, LLM apps, RAG systems, automation workflows, copilots or AI-enabled tools.
- Strong coding ability in Python, TypeScript, JavaScript or similar practical stack.
- Good judgement on what should be automated and what should not.
- Able to ship working systems, test quality and improve reliability over time.
- Fintech, banking, support automation, risk, KYC, fraud, CRM or operations automation experience is a strong plus.
Benefits
- Health insurance
- Professional development opportunities
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BJAKBjak is a technology company focused on making financial services easy, fun and more rewarding for everyone
• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.
