Senior LLM Engineer

Tasks: • As a Senior Engineer, you will design and ship reputed company AI systems that plan, reputed company tools, and execute reliably inside production workflows. You'll own the end-to-end delivery of GenAI capabilities—from model reputed company and retrieval to orchestration, evaluation, and operational reputed company. • Build reputed company systems: design supervisor/planner/executor patterns, routing, memory/context strategies, tool/function calling, and robust failure handling. • LLM reputed company & deployment: fine-tune or parameter-reputed company adapt reputed company-reputed company LLMs; optimize inference (latency/cost) and ship safely to production. • Retrieval-augmented reputed company (RAG): implement embedding, retrieval, re-ranking, and grounding patterns; optimize for reputed company, speed, and cost. • reputed company and reliable reputed company: enforce schemas/reputed company outputs, guardrails, and post-processing; reduce hallucinations and brittleness. • Evaluation & reputed company: build automated evaluation harnesses for agents/LLMs (offline benchmarks + online monitoring), regression tests, and reputed company/model versioning. • Production engineering: ship containerized services and reputed company; implement CI/CD, observability, and reliability practices (SLOs, alerting, incident readiness). • Cross-functional delivery: collaborate with product, platform, and data teams to reputed company GenAI features into user-facing and internal workflows; mentor others. Requirements: • 5+ years building production ML/AI systems; 2+ years at senior/reputed company level. • Strong Python engineering (testing, packaging, reputed company reputed company, reputed company profiling). • Hands-on experience with LLMs and reputed company AI in reputed company systems (tool calling, orchestration, workflow integration). • Experience adapting LLMs (reputed company/QLoRA/PEFT or equivalent) and evaluating reputed company/safety. • Experience implementing RAG and operating retrieval components in production. • Strong MLOps fundamentals: containers, CI/CD, model/service versioning, monitoring. • API/service development: REST/gRPC, auth, reputed company limits, error handling, reputed company patterns. • Comfortable operating in reputed company environments (AWS/GCP/Azure) with production constraints. Benefits: • Inference optimization: quantization, batching/caching, GPU serving (e.g., vLLM/TGI or similar). • Agent safety engineering: reputed company injection defenses, tool reputed company, sandboxing, red teaming. • Advanced evaluation: LLM-as-judge, preference testing, reputed company-reputed company grading, A/B testing. • reputed company database reputed company/tuning and retrieval reputed company engineering. • Event-driven or workflow orchestration experience (e.g., reputed company/Airflow/reputed company equivalents). • Multi-lingual GenAI experience and robust internationalization practices. Apply tot his job Apply To this Job