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Tier-1 brand and metro locations increase applicant density, but niche ML/LLM specialization limits broader applicant pool.
Specialized ML/LLM infrastructure skills transfer across industries, though financial services governance knowledge increases domain specificity.
Explicit 8+ years, mandatory ML systems experience and extensive tech stack increases rigid filtering.
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Design, develop, and deliver production-grade AI/ML software solutions including agentic workflows, LLM applications, evaluation pipelines, and MLOps tooling.
Lead and mentor engineering teams or collaborate across teams to drive technical delivery aligned with enterprise architecture and business objectives.
Own risk management, secure coding, and compliance aspects of AI software while influencing decision-making and policy development in technology operations.
Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or related discipline.
8+ years of software engineering experience including at least 3 years in building AI/ML systems or scalable ML infrastructure at enterprise scale.
Strong proficiency in Python (FastAPI, asyncio) and/or Go/Java with experience in building APIs and microservices for high-throughput, low-latency workloads.
Willingness to work onsite in Bengaluru or Pune, India (role based in these offices).
Experienced in developing advanced AI/ML systems including multi-agent orchestration, prompt engineering, RAG architectures, and deployment of complex AI workflows.
Strong knowledge of distributed systems, cloud services (AWS), containerization (Docker, Kubernetes), and infrastructure automation (CI/CD, IaC).
Ability to lead complex technical projects, consult on risk/control, communicate complex technical concepts, and influence stakeholders across business and technology teams.