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Tier-1 brand, metro location, and broad AI/MLOps skillset increase candidate competition.
Requires specialized ML/LLM, MLOps and agentic system experience, limiting cross-industry transferability.
Explicit 8+ years, mandatory ML systems experience and specific tech stack (Kubernetes, AWS, Python) enforce strict filtering.
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Lead design, development, and delivery of scalable, secure AI/ML software solutions powering enterprise AI capabilities, including agentic AI systems and LLM applications.
Own significant AI subsystems such as multi-agent orchestration, evaluation pipelines, and MLOps tooling with responsibility for production-grade implementations aligned to architectural vision.
Collaborate cross-functionally with product, design, and engineering teams; lead or guide junior engineers; consult on complex technical issues; and influence stakeholders.
Bachelor's degree or above in Computer Science, Engineering, Mathematics, or related discipline.
8+ years software engineering experience with at least 3 years building AI/ML systems or scalable ML infrastructure.
Proficiency in Python (including FastAPI, asyncio) and/or Go/Java with production experience developing APIs and microservices for high-throughput, low-latency workloads.
Experience with AI frameworks for agentic workflows, GenAI/LLM systems, distributed systems architecture, AWS services (EC2/EKS, S3, IAM, RDS, AI services), Docker, Kubernetes, CI/CD, and Infrastructure-as-Code.
Experienced in developing complex, production-grade AI/ML subsystems with hands-on knowledge of agent-based AI, prompt engineering, RAG architectures, and modern agent frameworks.
Skilled in distributed systems design, cloud-native development, and deployment with strong technical ownership from ideation to delivery in enterprise contexts.
Demonstrates leadership on technical delivery including mentoring, cross-functional collaboration, and influencing stakeholders across technical and business domains.