Sr. Machine Learning Engineer

Your Impact

• Asynchronous Systems: Architect and optimize high-throughput, event-driven systems using Apache Kafka to handle real-time data flows.
• Data Processing at Scale: Build and maintain large-scale data pipelines using Apache Spark or Flink to provide the high-volume analytics that power our AI.
• Agentic Systems at Scale: Design sophisticated AI Agents capable of autonomous planning, memory management, and high-reliability tool-use across distributed environments.
• Infrastructure & Orchestration: Lead the architectural design of containerized services on Kubernetes, ensuring high availability and scalability across Cloud Infrastructure (AWS/Azure/GCP).

Your Toolkit

• 5–8 years of experience in backend engineering using Java, Python, or Go.
• Expertise in distributed systems, asynchronous architectures (Kafka), and large-scale data processing (Spark/Flink).
• Hands on experience with agentic frameworks (e.g., AutoGen, CrewAI, or custom orchestration layers), RAG, MCP, fine tuning models and prompt engineering.
• Agentic observability using Langfuse, Evals frameworks for Testing/Resilience.

Bonus Points:

• Advanced IaC: Expertise in building reusable Terraform modules and managing complex multi-region cloud deployments.
• Vector DB Optimization: Deep experience in indexing strategies (HNSW vs IVF) and performance tuning for high-concurrency vector databases at scale.
• AI Ops: Experience with LLM deployment optimization (e.g., vLLM, TensorRT-LLM) or managing proprietary model inference endpoints.